Boom5426/Awesome-Virtual-Cell

Awesome-AI-Virtual-Cell: papers, datasets, benchmarks, talks, and community resources for AIVC

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Awesome Virtual Cell

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A curated gateway to papers, datasets, benchmarks, and community resources for AI-powered virtual cell research.

Virtual Cells · Perturbation · Intervention Design · Foundation Models · Spatial

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🚀 Start Here

New to virtual cell research? Pick a track instead of reading the full list from top to bottom.

Start with the bold entry in your track, then compare the other examples. These are selective reading suggestions, not a leaderboard; preprints and unpublished manuscripts are marked explicitly.

Goal	Recommended entry points
🧬 Understand the field	Cell perspective · Grow AI Virtual Cells · Nature perspective
🧫 Predict perturbations	GEARS · STATE · MAP · CellFlow (preprint)
🧠 Cellular foundation models	Geneformer · scGPT · scFoundation · UCE · TranscriptFormer
🌐 World models & cell-state transitions	A world model of the virtual cell · CellOS (preprint) · Chreode (preprint)
🖼️ Multimodal & spatial	Nicheformer · VirTues · DePass · UniPert-G2CP
🎯 Intervention design	PDGrapher · DrugReflector · CellNavi · PAIRING · VCDesign (manuscript)
📏 Evaluation & measurement	Systema · SCMBench · PertResolve (manuscript) · Signal, Bounds & Baselines (preprint) · Principled Evaluation (preprint)

🔬 Research Papers

Browse the full research collection below; each paper may carry multiple independently assigned tags.

327 papers · 2026 (222) · 2025 (87) · 2024 (18)

Papers can have multiple topics. Search and combine topics · Tag definitions

🗓️ 2026 — 222 papers

[Recoverable Resolution] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] The recoverable resolution of cellular perturbation-response prediction (bioRxiv 2026) [preprint] [code] [source data]
[Pop-Corn] [Perturbation] [Spatial] [Intervention Design] Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts (bioRxiv 2026) [preprint]
[NexuST] [Foundation Model] [Spatial] [Representation Learning] NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics (bioRxiv 2026) [preprint]
[EpiZoo] [Foundation Model] [Gene Regulation] [Multimodal] [Representation Learning] EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics (bioRxiv 2026) [preprint]
[PerturbBridge] [Perturbation] [Dynamics] [Representation Learning] PerturbBridge: Conditional Latent Schrödinger Bridge for Single-Cell Perturbation Response Prediction (bioRxiv 2026) [preprint]
[BioPert] [Perturbation] [Representation Learning] A biological-response compound representation allows chemical perturbation prediction across cell lines (bioRxiv 2026) [preprint]
[SPECTRA] [Perturbation] [Gene Regulation] SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks (bioRxiv 2026) [preprint]
[ISP³ Platform] [Tool] [Foundation Model] [Perturbation] [Intervention Design] [Dynamics] ISP³ Platform powered by Geneformer: Framework for Cross-Species, Sequential, and Multi-Gene In Silico Perturbation Screens with Application to iPS Cell State Transitions (bioRxiv 2026) [preprint]
[Aging scFM Benchmark] [Benchmark] [Foundation Model] [Evaluation & Measurement] [Related] Benchmarking single-cell foundation models for aging biology (bioRxiv 2026) [preprint]
[CellRFT] [Perturbation] [Evaluation & Measurement] CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling (arXiv 2026) [preprint]
[PopPert] [Perturbation] PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction (arXiv 2026) [preprint] [code]
[STP-BENCH] [Benchmark] [Spatial] [Morphology] [Evaluation & Measurement] STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images (arXiv 2026) [preprint] [code]
[RAGCell] [Foundation Model] [Multimodal] [Representation Learning] RAGCell: Retrieval-Augmented Generation as Supervision for Versatile Single-cell Analysis (arXiv 2026) [preprint]
[stFormer] [Foundation Model] [Spatial] [Gene Regulation] [Representation Learning] stFormer integrates spatial ligand signaling into a foundation model for spatial transcriptomics (Cell Reports Methods 2026) [paper]
[scLDM] [Perturbation] [Dynamics] scLDM: a conditional diffusion framework for single-cell perturbation prediction (Bioinformatics 2026) [paper] [code]
[CSGDA] [Perturbation] [Representation Learning] CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction (Bioinformatics 2026) [paper] [preprint]
[DePass] [Tool] [Spatial] [Multimodal] [Representation Learning] The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics (Nature Cell Biology 2026) [paper] [code] [documentation]
[IRIS] [Perturbation] [Dynamics] [Intervention Design] [Dataset] Reconstructing signaling histories of single cells via perturbation screens and transfer learning (Nature Methods 2026) [paper] [dataset]
[MAP] [Perturbation] [Foundation Model] [Multimodal] [Representation Learning] A knowledge-driven framework for predicting single-cell responses for unprofiled drugs (Nature Machine Intelligence 2026) [paper] [preprint] [code] [dataset]
[SCMBench] [Benchmark] [Foundation Model] [Multimodal] [Evaluation & Measurement] SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration (Nature Communications 2026) [paper] [code]
[GeneformerV2] [Foundation Model] [Representation Learning] [Gene Regulation] [Perturbation] [Tool] Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology (Nature Computational Science 2026) [paper] [code] [dataset] [documentation]
[CRISP] [Foundation Model] [Perturbation] Predicting drug responses of unseen cell types through transfer learning with foundation models (Nature Computational Science 2026) [paper] [code]
[XPert] [Perturbation] [Dynamics] [Gene Regulation] Modelling drug-induced cellular perturbation responses with a biologically informed dual-branch transformer (Nature Machine Intelligence 2026) [paper] [code] [dataset]
[Trustworthy Virtual Cells] [Virtual Cell] [Review] [Evaluation & Measurement] [Perturbation] Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models (Frontiers in Cell and Developmental Biology 2026) [paper]
[scDMC] [Foundation Model] [Representation Learning] [Gene Regulation] scDMC: Unlocking biological insight from single-cell data with an interpretable dual-stream foundation model (Genome Biology 2026) [paper]
[CellVQ] [Foundation Model] [Representation Learning] CellVQ: Illuminating cell states by a comprehensive and interpretable single cell foundation model (Nature Communications 2026) [paper]
[SpatialFormer] [Foundation Model] [Spatial] [Multimodal] [Representation Learning] SpatialFormer: universal spatial representation learning from subcellular molecular to multicellular landscapes (Nature Computational Science 2026) [paper]
[VirTues] [Virtual Cell] [Foundation Model] [Spatial] [Protein] [Representation Learning] The Virtual Tissues foundation model resolves spatial proteomics across scales (Nature 2026) [paper]
[HEX] [Spatial] [Morphology] [Protein] [Multimodal] AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer (Nature Medicine 2026) [paper]
[spEMO] [Foundation Model] [Spatial] [Morphology] [Multimodal] [Representation Learning] Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology Data (Nature Biomedical Engineering 2026) [paper]
[Scaling Is Much Pain] [Foundation Model] [Benchmark] [Evaluation & Measurement] Scaling up training dataset size for transcriptomic AI models is much pain with little gain (Nature Methods 2026) [paper]
[Biomedical FM Benchmark] [Foundation Model] [Benchmark] [Evaluation & Measurement] [Related] Benchmarking biomedical foundation models (Nature Methods 2026) [paper]
[scTranslation] [Benchmark] [Multimodal] [Evaluation & Measurement] scTranslation: A Comprehensive Benchmark for Single-Cell Multi-Omics Modality Translation (KDD 2026) [paper]
[Interpretation, Extrapolation & Perturbation] [Review] [Foundation Model] [Perturbation] [Gene Regulation] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]
[AI Digital Organism] [Review] [Virtual Cell] [World Model] [Related] How to build an AI-driven digital organism (Nature Medicine 2026) [paper]
[World Models for Biomedicine] [World Model] [Review] [Related] World models for biomedicine (Cell 2026) [paper]
[Fifteen Challenges] [Review] [Virtual Cell] [Related] Fifteen challenges for generative AI applications to cell biology (Cell 2026) [paper]
[Compositional Foundation Models] [Review] [Foundation Model] [Multimodal] From modality-specific to compositional foundation models for cell biology (Cell Systems 2026) [paper]
[Nuisance Robustness] [Foundation Model] [Benchmark] [Evaluation & Measurement] Robustness to nuisance perturbations enables unsupervised evaluation of single-cell foundation models (bioRxiv 2026) [preprint]
[Scaling Recipes] [Foundation Model] [Benchmark] [Evaluation & Measurement] Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold? (bioRxiv 2026) [preprint]
[Accessible scFM Deployment] [Foundation Model] [Evaluation & Measurement] [Tool] Accessible and reproducible deployment reveals the practical boundaries of single-cell foundation models (bioRxiv 2026) [preprint]
[Parameter-Free Representations] [Foundation Model] [Benchmark] [Evaluation & Measurement] [Representation Learning] [Related] Parameter-free representations outperform single-cell foundation models on downstream benchmarks (arXiv 2026) [preprint]
[PertResolve] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] [Tool] Measurement resolution constrains fine-grained perturbation prediction (Manuscript 2026) [preprint] [code] [project] [dataset]
[Signal, Bounds & Baselines] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] Signal, Bounds, and Baselines: Principles for Evaluating Virtual Cell Perturbation Models (bioRxiv 2026) [preprint] [code]
[Metric Failure Modes] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] Evaluating Single-Cell Perturbation Response Models Is Far from Straightforward (bioRxiv 2026) [preprint]
[VCBench (In-the-Wild)] [Benchmark] [Evaluation & Measurement] [Perturbation] [Virtual Cell] Benchmarking virtual cell models for in-the-wild perturbation response (arXiv 2026) [preprint] [code] [project]
[VCDesign] [Intervention Design] [Perturbation] [Virtual Cell] VCDesign: Finite-Budget Intervention Design for Virtual Cells (Manuscript 2026) [preprint] [code] [project] [dataset]
[NUDGE] [Intervention Design] [Gene Regulation] [Dynamics] [Related] Uncovering minimal control of cell fate by natural dynamics (PNAS 2026) [paper] [code]
[Speciesformer] [Virtual Cell] [Foundation Model] [Multimodal] [Perturbation] [Representation Learning] Speciesformer learns conserved cellular states for cross-species generative virtual cell modeling (bioRxiv 2026) [preprint]
[DeepSCENIC] [Gene Regulation] [Multimodal] [Perturbation] DeepSCENIC: transfer learning from sequence-to-function models enables causal gene regulatory network inference (bioRxiv 2026) [preprint] [code]
[scKITE] [Foundation Model] [Representation Learning] Towards a knowledge-enhanced single-cell foundation model (arXiv 2026) [preprint] [code]
[PHAROS] [Perturbation] [Intervention Design] [Virtual Cell] PHAROS: turning single-cell perturbation models into target-directed drug-combination screens (bioRxiv 2026) [preprint] [code] [reproduce]
[ProteinTalks] [Virtual Cell] [Foundation Model] [Perturbation] [Protein] [Dynamics] [Intervention Design] An operational perturbation proteomics-based virtual cell model (Nature 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[Virtual Cell Challenge 2026] [Benchmark] [Perturbation] Virtual Cell Challenge 2026: Benchmarking zero-shot generalization across cellular contexts (Cell 2026) [paper] [challenge]
[LucaCell] [Foundation Model] [Representation Learning] [Multimodal] LucaCell: a sequence-centric foundation model for cross-species single-cell analysis (bioRxiv 2026) [preprint] [code]
[AnnFlux] [Perturbation] [Dynamics] AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics (bioRxiv 2026) [preprint]
[scRep] [Foundation Model] [Representation Learning] scRep: A Latent-Space Self-Distilled Foundation Model for Single-Cell Representation Learning (bioRxiv 2026) [preprint]
[Cell-o1] [Agent] [Benchmark] Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning (Bioinformatics 2026) [paper] [preprint] [code] [hugging face] [ask deepwiki]
[RegVelo] [Dynamics] [Gene Regulation] [Perturbation] [Multimodal] RegVelo: Gene-Regulatory-Informed Dynamics of Single Cells (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[TranscriptFormer] [Foundation Model] [Representation Learning] TranscriptFormer: A Generative Cell Atlas across 1.5 Billion Years of Evolution (Science 2026) [paper] [preprint] [code] [ask deepwiki]
[CellAtria] [Agent] [Tool] An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-Seq Data Analysis (npj Artificial Intelligence 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[CellVoyager] [Agent] [Benchmark] CellVoyager: AI CompBio Agent Generates New Insights by Autonomously Analyzing Biological Data (Nature Methods 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[Biomni] [Agent] [Related] Autonomous Biomedical Research with an Artificial Intelligence Agent (Science 2026) [paper] [preprint] [code] [ask deepwiki]
[STATE] [Perturbation] [Foundation Model] [Benchmark] Predicting cellular responses to perturbation across diverse contexts with State (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[UniPert-G2CP] [Perturbation] [Multimodal] UniPert-G2CP Bridges Genetic and Chemical Screens from Molecular Representation to Phenotype Modeling (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[Cell Shapes] [Morphology] [Protein] [Perturbation] Cell shapes decode molecular phenotypes in image-based spatial proteomics (Cell Systems 2026) [paper] [preprint] [code]
[scLong] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics (Nature Communications 2026) [paper] [preprint] [code] [ask deepwiki]
[UCE] [Foundation Model] [Representation Learning] Universal Cell Embedding Provides a Foundation Model for Cell Biology (Nature 2026) [paper] [preprint] [code] [ask deepwiki]
[CellOS] [Virtual Cell] [World Model] [JEPA] [Foundation Model] [Representation Learning] [Multimodal] [Perturbation] CellOS: Learning a World Model of Cellular State through Joint Embedding Prediction (bioRxiv 2026) [preprint]
[Chreode] [World Model] [Foundation Model] [Dynamics] [Perturbation] Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction (arXiv 2026) [preprint] [code]
[VCWM] [Virtual Cell] [World Model] [Review] A world model of the virtual cell (Cell 2026) [paper] [technical report]
[World Model Gaps] [World Model] [Virtual Cell] [Benchmark] [Evaluation & Measurement] What Makes a Virtual Cell a World Model? Three Gaps, Three Experiments, and a Roadmap (Research Square 2026) [preprint]
[Biomedical World Models] [World Model] [Review] [Related] Towards World Models in Biomedical Research (arXiv 2026) [preprint]
[CENO] [World Model] [Foundation Model] [Gene Regulation] [Intervention Design] [Related] CENO: A Genome-Scale World Model for Evolutionary Sequence Interpretation and Programmable Regulatory Design (bioRxiv 2026) [preprint] [code]
[Cell-JEPA] [JEPA] [Foundation Model] [Representation Learning] [Perturbation] Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics (arXiv 2026) [preprint]
[SpatialJEPA] [JEPA] [Spatial] [Multimodal] [Representation Learning] SpatialJEPA: JEPA-Inspired Graph-Context Distillation for Spatially Aware Multiomics Integration (bioRxiv 2026) [preprint] [code]
[BioM-JEPA] [JEPA] [Foundation Model] [Representation Learning] [Perturbation] BioM-JEPA: Joint-Embedding Prediction of Graph-Connected Gene Blocks in Single Cells (arXiv 2026) [preprint]
[CellWorld] [JEPA] [Foundation Model] [Spatial] [Representation Learning] CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models (arXiv 2026) [preprint] [code]
[TERRA] [JEPA] [Foundation Model] [Spatial] [Perturbation] [Representation Learning] Multi-Scale Modeling of Human Tissues from Spatial Transcriptomics with TERRA (bioRxiv 2026) [preprint] [code]
[Context Not Scale] [Virtual Cell] [Review] [Perturbation] Position: Virtual Cells Need Context, Not Just Scale (ICML 2026) [paper] [related implementation]
[scDifformer] [Virtual Cell] [Foundation Model] [Representation Learning] [Perturbation] scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data (Nucleic Acids Research 2026) [paper] [code] [project]
[OCellus] [Virtual Cell] [Foundation Model] [Multimodal] [Spatial] [Perturbation] OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning (bioRxiv 2026) [preprint]
[CellQ / PACE] [Virtual Cell] [Agent] [Intervention Design] [Perturbation] Virtual-cell verification enables self-auditing AI discovery for immune rejuvenation (bioRxiv 2026) [preprint]
[VCHarness] [Agent] [Virtual Cell] Harnessing AI to Build Virtual Cells (bioRxiv 2026) [preprint] [code]
[VCR-Agent] [Agent] [Virtual Cell] [Perturbation] [Gene Regulation] Towards Autonomous Mechanistic Reasoning in Virtual Cells (arXiv 2026) [preprint] [code]
[SpaCellAgent] [Agent] [Dynamics] [Spatial] [Tool] SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis (arXiv 2026) [preprint] [code]
[CellConsensus] [Agent] [Tool] [Dataset] CellConsensus: An agent-curated atlas for automatic cell typing (bioRxiv 2026) [preprint] [code]
[Literature-Authored Embeddings] [Agent] [Representation Learning] Coding agents author interpretable single-cell embedding models from the literature (bioRxiv 2026) [preprint] [code]
[LLM4Cell] [Benchmark] [Agent] [Review] LLM4Cell: Taxonomy and Evaluation of LLM and Agentic Models for Single-Cell Biology (ACL 2026) [paper]
[scBench-Long] [Benchmark] [Agent] [Multimodal] scBench-Long: Verifiable Benchmarking of Long-Horizon Single-Cell Biology (arXiv 2026) [preprint] [code]
[Score Distributions] [Benchmark] [Perturbation] [Evaluation & Measurement] Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap (arXiv 2026) [preprint]
[Projection Basis] [Benchmark] [Perturbation] [Representation Learning] [Evaluation & Measurement] The projection basis determines the information ceiling for perturbation prediction (bioRxiv 2026) [preprint]
[Harmonised FM Benchmark] [Benchmark] [Foundation Model] [Spatial] [Perturbation] Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation (arXiv 2026) [preprint] [code]
[scContam] [Benchmark] [Foundation Model] [Evaluation & Measurement] Auditing pretraining contamination in single-cell foundation model benchmarks (arXiv 2026) [preprint] [code]
[PertReason] [Benchmark] [Perturbation] [Agent] [Gene Regulation] PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects (arXiv 2026) [preprint] [dataset]
[DE Classification] [Benchmark] [Perturbation] [Evaluation & Measurement] Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models (bioRxiv 2026) [preprint]
[Principled Evaluation] [Benchmark] [Perturbation] [Evaluation & Measurement] Towards Principled Evaluation of Single-Cell Perturbation Prediction Models (bioRxiv 2026) [preprint] [code]
[Response Magnitude] [Benchmark] [Perturbation] [Foundation Model] [Evaluation & Measurement] Response Magnitude as a Dominant Signal for Held-Out CRISPRi Perturbation Effect Prediction (arXiv 2026) [preprint]
[SAFFRON] [Benchmark] [Foundation Model] [Spatial] Evaluating the ability of spatial transcriptomics foundation models to learn multi-scale spatial variation (bioRxiv 2026) [preprint] [code]
[Confound Diagnostics] [Benchmark] [Foundation Model] [Perturbation] [Tool] [Evaluation & Measurement] A confound-diagnostic toolkit for in silico perturbation with single-cell foundation models (bioRxiv 2026) [preprint] [code]
[Reliable Perturbations] [Benchmark] [Perturbation] [Evaluation & Measurement] Reliable single-cell perturbations explain and improve model performance (bioRxiv 2026) [preprint]
[Cell Line Bottleneck] [Benchmark] [Perturbation] [Evaluation & Measurement] Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale (bioRxiv 2026) [preprint] [code]
[ST Agent Benchmark] [Benchmark] [Agent] [Spatial] Mind the alignment gap: a spatial transcriptomics benchmark for scientific coding agents (bioRxiv 2026) [preprint]
[CRISPRko vs CRISPRi] [Benchmark] [Dataset] [Perturbation] Direct comparison of CRISPR knockout and interference with Perturb-seq (bioRxiv 2026) [preprint] [code]
[JUMP-lite] [Benchmark] [Morphology] [Dataset] [Tool] JUMP-lite: Compact, reproducible benchmarking of cell representations (arXiv 2026) [preprint] [code]
[scVision] [Foundation Model] [Representation Learning] A vision foundation model for single-cell biology via spatial gene cartography (arXiv 2026) [preprint] [project]
[SATScG] [Foundation Model] [Representation Learning] Scaling an Autoregressive Transformer for Single-Cell Generation (arXiv 2026) [preprint] [code]
[Gene Intelligence] [Foundation Model] [Representation Learning] [Benchmark] Raw-count embeddings improve single-cell foundation models (bioRxiv 2026) [preprint] [code]
[Task-Adapted FM] [Foundation Model] [Perturbation] [Representation Learning] Task-adapted biological foundation models uncover perturbation-centric representations (bioRxiv 2026) [preprint] [code]
[Species-Native Tokens] [Foundation Model] [Representation Learning] Single-cell foundation modeling with species-native protein tokens links regenerative competence across frog and mouse (bioRxiv 2026) [preprint] [code]
[Complementary Views] [Foundation Model] [Representation Learning] [Gene Regulation] Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views (arXiv 2026) [preprint]
[Tabula] [Foundation Model] [Gene Regulation] [Intervention Design] Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning (arXiv 2026) [preprint] [code]
[AdaGeneBudget] [Foundation Model] [Tool] [Representation Learning] AdaGeneBudget: Cell-Adaptive Gene-Token Allocation for Efficient Single-Cell Foundation Models (bioRxiv 2026) [preprint]
[CellTosg2Sequence] [Foundation Model] [Multimodal] [Representation Learning] CellTosg2Sequence: A Unified Text-Omics-Signaling-Graph Large Language Model for Single-Cell Analysis (bioRxiv 2026) [preprint]
[Stable-Shift] [Perturbation] Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations (arXiv 2026) [preprint] [code]
[PertOmni] [Perturbation] [Multimodal] [Morphology] [Representation Learning] Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings (bioRxiv 2026) [preprint]
[scCycleMol] [Perturbation] Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses (arXiv 2026) [preprint]
[GenPerturb] [Perturbation] [Gene Regulation] GenPerturb: sequence-grounded interpretation of perturbation transcriptomes using pretrained genomic models (bioRxiv 2026) [preprint] [code]
[U-Pert] [Perturbation] [Dynamics] [Intervention Design] Unbalanced Perturbation Dynamics For Cell Fate Design (bioRxiv 2026) [preprint] [code]
[GeneSpeak-FP] [Perturbation] [Intervention Design] GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures (arXiv 2026) [preprint]
[PerturbPFN] [Perturbation] [Foundation Model] [Gene Regulation] PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling (ICML 2026) [paper]
[Response Decomposition] [Perturbation] [Benchmark] Perturbation response decomposition enables biologically aligned generalization to unseen perturbations and cellular contexts (bioRxiv 2026) [preprint] [code]
[Cytokine Atlas] [Perturbation] [Gene Regulation] [Dataset] A human cytokine response atlas to reconstruct underlying gene regulatory networks (bioRxiv 2026) [preprint] [code] [project] [dataset]
[PerturbMap] [Perturbation] PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses (arXiv 2026) [preprint]
[LGR] [Perturbation] [Agent] LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses (ICLR 2026) [paper]
[MEGA-ODE] [Perturbation] [Dynamics] [Gene Regulation] [Intervention Design] MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics (bioRxiv 2026) [preprint] [code]
[TranScouter] [Perturbation] [Benchmark] A structured study of cross-condition prediction of transcriptional responses to gene perturbations (bioRxiv 2026) [preprint] [code]
[COMPASS] [Perturbation] COMPASS: Component-Wise Inference of Shared and Gene-Specific Perturbation Response (bioRxiv 2026) [preprint] [code]
[SLIM] [Perturbation] SLIM: A small linear model with STRING embeddings for single-cell genetic perturbation prediction (bioRxiv 2026) [preprint] [code]
[GeneGeoFlow] [Perturbation] Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling (arXiv 2026) [preprint]
[PerturbLDM] [Perturbation] PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responses (bioRxiv 2026) [preprint] [code]
[Coladan] [Spatial] [Morphology] [Multimodal] [Perturbation] [Dataset] Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts (Genome Medicine 2026) [paper] [code]
[Spaceland] [Spatial] [Morphology] [Multimodal] Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics (bioRxiv 2026) [preprint] [code (project)]
[TissueFormer (Pathology)] [Spatial] [Morphology] [Multimodal] [Foundation Model] Multi-Modal Foundation Model with Whole-Slide Attention Enables Transferrable Digital Pathology at Single-Cell Resolution (bioRxiv 2026) [preprint] [code]
[VOICE] [Spatial] [Morphology] [Multimodal] [Foundation Model] VOICE: A Vision-Omics Foundation Model Integrating Direct and Retrieval-Based Prediction of In-situ Single-Cell Gene Expression (arXiv 2026) [preprint] [code]
[VISTA] [Spatial] [Morphology] [Multimodal] Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer (bioRxiv 2026) [preprint] [code]
[SQUINT] [Spatial] [Representation Learning] Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization (bioRxiv 2026) [preprint] [code (project)]
[MAE-3D] [Morphology] [Multimodal] [Representation Learning] [Benchmark] 3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy (arXiv 2026) [preprint] [code]
[Cell Painting RAG Audit] [Morphology] [Agent] [Benchmark] Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology (arXiv 2026) [preprint]
[Spatium] [Protein] [Spatial] [Foundation Model] [Representation Learning] Spatium: A Protein Language Foundation Model for Spatial Proteomics (bioRxiv 2026) [preprint] [code]
[PerturbMatch] [Perturbation] [Tool] Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq (bioRxiv 2026) [preprint] [code]
[scRepresenter] [Representation Learning] [Tool] [Benchmark] [Foundation Model] scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics (bioRxiv 2026) [preprint] [code]
[CELLens] [Virtual Cell] [Gene Regulation] [Tool] Human-Guided Causal Knowledge Injection for Virtual Cells (arXiv 2026) [preprint] [code]
[Tabular FM Perturbation] [Benchmark] [Perturbation] [Foundation Model] Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales (bioRxiv 2026) [preprint] [code]
[CellFM-Datasets] [Foundation Model] [Spatial] [Tool] Cellfm-datasets: A Unified Data Infrastructure for Single-Cell and Spatial Transcriptomics Foundation Model Pretraining (bioRxiv 2026) [preprint]
[OCOO-T] [Virtual Cell] [Perturbation] OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction (arXiv 2026) [preprint]
[Glitch Genes] [Benchmark] [Foundation Model] [Representation Learning] Glitch genes: embedding geometry predicts functional fragility in single-cell foundation models (bioRxiv 2026) [preprint]
[VCBench] [Virtual Cell] [Benchmark] [Foundation Model] VCBench: A Multi-Dimensional Benchmark for Single-Cell Foundation Models (bioRxiv 2026) [preprint] [code]
[Design Space] [Perturbation] [Benchmark] [Representation Learning] Elucidating the Design Space of Generative Models for Single-Cell Perturbation Prediction (bioRxiv 2026) [preprint] [code]
[PertDiffBench] [Perturbation] [Benchmark] PertDiffBench: Benchmarking Diffusion Models for Single-Cell Perturbation Response Prediction (bioRxiv 2026) [preprint] [code (project)]
[Zero-Shot Benchmark] [Benchmark] [Foundation Model] [Representation Learning] Systematic benchmarking of zero-shot utility and robustness in single-cell transcriptomic foundation models (bioRxiv 2026) [preprint] [code]
[DeepSpot-M] [Spatial] [Morphology] [Multimodal] [Foundation Model] DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology (medRxiv 2026) [paper] [code]
[V3Cell] [Virtual Cell] [Morphology] [Perturbation] [Dynamics] V3Cell: A Vision-Guided Virtual 3D Cell Framework for Phenotypic Modeling and Perturbation Prediction (bioRxiv 2026) [preprint] [code]
[Cross-Context DrugPert] [Perturbation] [Multimodal] Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework (Bioinformatics 2026) [paper] [code]
[SciCore-Omics] [Foundation Model] [Spatial] [Morphology] [Multimodal] SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology (bioRxiv 2026) [preprint] [code]
[HoloCell] [Virtual Cell] [Foundation Model] [Multimodal] [Protein] HoloCell: A Generative Foundation Model for Holistic Cellular Modeling (bioRxiv 2026) [preprint] [project (code pending)]
[FM Roadmap] [Foundation Model] [Spatial] [Review] A User’s Roadmap to Foundation Models on Single-Cell and Spatial-Omics – Cell Type and Lineage applications (National Science Review 2026) [paper]
[PerturbCellRL] [Perturbation] PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction (arXiv 2026) [preprint]
[KG-Reasoning LLM] [Perturbation] [Agent] Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors (arXiv 2026) [preprint]
[Cross-Modal Transfer] [Foundation Model] [Multimodal] [Spatial] Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models (arXiv 2026) [preprint]
[BRIDGE] [Foundation Model] [Morphology] [Spatial] [Multimodal] BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer Diagnosis (bioRxiv 2026) [preprint] [code]
[Morphodynamics-Expr] [Morphology] [Dynamics] [Multimodal] Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change (Cell Systems 2026) [paper] [code] [implementation package]
[Morph-Transcriptomic GenModel] [Perturbation] [Morphology] [Multimodal] A generative framework for predicting cellular morphological and transcriptomic perturbation responses (Cell Reports Methods 2026) [paper] [code]
[DoFormer] [Perturbation] [Gene Regulation] [Multimodal] DoFormer: Causal Transformer for Gene Perturbation (bioRxiv 2026) [preprint]
[RegFormer] [Foundation Model] [Gene Regulation] [Representation Learning] RegFormer: a single-cell foundation model powered by gene regulatory hierarchies (Nature Communications 2026) [paper] [code]
[Spurious Correlation] [Benchmark] [Perturbation] [Evaluation & Measurement] Spurious correlation inflates performance in single-cell perturbation prediction (bioRxiv 2026) [preprint] [code]
[scArchon] [Benchmark] [Perturbation] [Tool] scArchon: a scalable benchmarking framework for assessing single-cell perturbation models (Genome Biology 2026) [paper] [code]
[Chemical Pert DL] [Benchmark] [Perturbation] [Evaluation & Measurement] Deep learning models for chemical perturbation prediction do not yet utilise drug molecular features (bioRxiv 2026) [preprint]
[Cycle-Consistent GenModel] [Spatial] [Multimodal] [Protein] [Representation Learning] Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities (bioRxiv 2026) [preprint] [code]
[StateXDiff] [Perturbation] [Multimodal] StateXDiff: Cell State-Contextualized Multimodal Diffusion for Single-Cell Perturbation Prediction (arXiv 2026) [preprint]
[DeSCOPE] [Perturbation] [Multimodal] Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE (bioRxiv 2026) [preprint] [code]
[Dataset Size & Diversity] [Foundation Model] [Benchmark] Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance (Nature Methods 2026) [paper] [code]
[Lingshu-Cell] [Virtual Cell] [World Model] [Foundation Model] [Perturbation] Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells (arXiv 2026) [preprint] [code] [homepage]
[SCALE] [Perturbation] SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction (arXiv 2026) [preprint]
[Conditional Monge Gap] [Perturbation] Conditional Monge Gap enables generalizable single-cell perturbation modelling (Nature Machine Intelligence 2026) [paper] [code]
[ProtiCelli] [Virtual Cell] [Protein] [Morphology] Generative machine learning unlocks the first proteome-wide image of human cells (bioRxiv 2026) [preprint] [code]
[AetherCell] [Virtual Cell] [Foundation Model] [Perturbation] [Intervention Design] AetherCell: A generative engine for virtual cell perturbation and in vivo drug discovery (bioRxiv 2026) [preprint] [code]
[AlphaCell] [Virtual Cell] [World Model] [Perturbation] [Dynamics] Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell (bioRxiv 2026) [preprint]
[VCWorld] [Virtual Cell] [World Model] [Foundation Model] [Perturbation] VCWorld: A Biological World Model for Virtual Cell Simulation (ICLR 2026) [paper] [code] [ask deepwiki]
[Spatial Perturb-seq] [Perturbation] [Spatial] [Dataset] Spatial perturb-seq: single-cell functional genomics within intact tissue architecture (Nature Communications 2026) [paper] [code]
[Celcomen] [Perturbation] [Spatial] [Gene Regulation] Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling (Nature Communications 2026) [paper] [code]
[stVCR] [Spatial] [Dynamics] stVCR: spatiotemporal dynamics of single cells (Nature Methods 2026) [paper] [code]
[CONCORD] [Representation Learning] Revealing a coherent cell-state landscape across single-cell datasets with CONCORD (Nature Biotechnology 2026) [paper] [code]
[AI Scientist] [Agent] [Related] Towards end-to-end automation of AI research (Nature 2026) [paper] [code] [template-free code]
[Conformation Description Language] [Protein] [Multimodal] [Related] Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language (Nature Machine Intelligence 2026) [paper] [code]
[CRISPRi Map] [Perturbation] [Gene Regulation] [Dataset] A genome-scale single-cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines (Cell Genomics 2026) [paper] [code] [publisher]
[TxPert] [Perturbation] TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects (Nature Biotechnology 2026) [paper] [code]
[Therapeutic Design] [Perturbation] [Intervention Design] Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes (Cell 2026) [paper] [code]
[X-Pert] [Perturbation] [Multimodal] Unified Multimodal Learning Enables Generalized Cellular Response Prediction to Diverse Perturbations (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[MVCBench] [Benchmark] [Perturbation] [Multimodal] [Morphology] MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[HarmonyCell] [Agent] [Perturbation] [Tool] HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts (bioRxiv 2026) [preprint]
[scDFM] [Perturbation] scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction (ICLR 2026) [paper] [code]
[Doloris] [Perturbation] Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation (ICLR 2026) [paper] [code]
[Departures] [Perturbation] Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges (AAAI 2026) [paper] [preprint] [code]
[PETRI] [Foundation Model] [Multimodal] [Representation Learning] PETRI: Learning Unified Cell Embeddings from Unpaired Modalities via Early-Fusion Joint Reconstruction (ICLR 2026) [paper]
[STRAND] [Perturbation] [Gene Regulation] STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations (arXiv 2026) [preprint]
[PerturbDiff] [Perturbation] PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling (arXiv 2026) [preprint] [code] [project]
[scBIG] [Perturbation] [Representation Learning] [Gene Regulation] Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction (arXiv 2026) [preprint] [code]
[CellxPert] [Foundation Model] [Perturbation] [Multimodal] [Spatial] [Protein] CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation (ICLR 2026) [paper]
[Perturbation Representation] [Perturbation] [Representation Learning] [Benchmark] [Evaluation & Measurement] What Makes a Representation Good for Single-Cell Perturbation Prediction? (arXiv 2026) [preprint] [code]
[CisTransCell] [Perturbation] [Gene Regulation] [Multimodal] CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context (arXiv 2026) [preprint]
[Latent Causal Processes] [Perturbation] [Dynamics] [Gene Regulation] Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction (arXiv 2026) [preprint] [code]
[msInfer] [Protein] [Multimodal] [Tool] Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer (Research Square 2026) [preprint] [code]
[Stack] [Foundation Model] [Representation Learning] [Perturbation] Stack: In-Context Learning of Single-Cell Biology (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[BioWorldModel] [World Model] [Dynamics] [Related] BioWorldModel: a single architecture predicts phenotype from genotype across four kingdoms of life (bioRxiv 2026) [preprint]
[Gene Importance] [Foundation Model] [Gene Regulation] [Tool] Scoring gene importance by interpreting single-cell foundation models (Nature Biotechnology 2026) [paper] [code]
[Hi-C FM] [Foundation Model] [Gene Regulation] [Multimodal] A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species (Nature Methods 2026) [paper] [code]
[Virtual Spatial Tumor] [Morphology] [Spatial] [Protein] [Multimodal] Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology (Cell 2026) [paper] [code]
[SynCell] [Virtual Cell] [Review] [Related] A framework for building a synthetic cell from the SynCell Asia Initiative (Nature Biotechnology 2026) [paper]
[3D Genome FM] [Foundation Model] [Gene Regulation] [Review] A foundation model to help understand the regulatory implications of 3D genome organization (Nature Methods 2026) [paper]
[TissueFormer (Population)] [Foundation Model] [Representation Learning] Tissueformer: extending single-cell foundation models to predict population-level phenotypes (BMC Bioinformatics 2026) [paper] [code]
[CytoSignal] [Spatial] [Dynamics] [Gene Regulation] [Tool] CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data (Nature Genetics 2026) [paper] [code]
[graphene-seq] [Spatial] [Multimodal] [Dynamics] [Dataset] In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments (Nature Communications 2026) [paper] [code]
[Deep Molecular Profiling] [Spatial] [Multimodal] [Review] Deep molecular profiling in three dimensions (Nature Methods 2026) [paper]
[SpaMosaic] [Spatial] [Multimodal] [Protein] [Representation Learning] Mosaic integration of spatial multi-omics with SpaMosaic (Nature Genetics 2026) [paper] [code]
[Computational Landscape] [Perturbation] [Review] Charting the computational landscape of single-cell genetic perturbation (Journal of Advanced Research 2026) [paper]
[veloAgent] [Dynamics] [Spatial] [Intervention Design] Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent (Molecular Systems Biology 2026) [paper] [code]
[Morphodynamics] [Morphology] [Dynamics] Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation (Molecular Systems Biology 2026) [paper] [code] [image-processing code]
[Single Cell Notebooks] [Tool] [Spatial] The Single Cell Notebooks for inclusive and accessible training in single-cell and spatial omics (Nature Genetics 2026) [paper] [code]
[CAPTAIN] [Foundation Model] [Multimodal] [Protein] [Representation Learning] CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein (Nature Communications 2026) [paper] [preprint] [code]
[scpFormer] [Foundation Model] [Protein] [Representation Learning] scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics (arXiv 2026) [preprint] [code]
[MultiPert] [Perturbation] [Multimodal] [Protein] MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction (PLOS Computational Biology 2026) [paper] [code]

🗓️ 2025 — 87 papers

[PDGrapher] [Perturbation] [Intervention Design] [Gene Regulation] [Representation Learning] Combinatorial prediction of therapeutic perturbations using causally inspired neural networks (Nature Biomedical Engineering 2025) [paper] [preprint] [code] [project]
[PAIRING] [Perturbation] [Intervention Design] [Representation Learning] Identifying an optimal perturbation to induce a desired cell state by generative deep learning (Cell Systems 2025) [paper] [code]
[ARC] [Intervention Design] [Gene Regulation] [Dynamics] [Related] Reverse control of biological networks to restore phenotype landscapes (Science Advances 2025) [paper] [code] [software archive]
[PerturbNet] [Perturbation] [Intervention Design] [Representation Learning] PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations (Molecular Systems Biology 2025) [paper] [preprint] [code]
[GeneJEPA] [JEPA] [World Model] [Foundation Model] [Representation Learning] [Perturbation] GeneJEPA: A Predictive World Model of the Transcriptome (bioRxiv 2025) [preprint] [code]
[STELLA] [Agent] [Multimodal] [Related] STELLA: Towards a Biomedical World Model with Self-Evolving Multimodal Agents (bioRxiv 2025) [preprint] [code]
[scPRINT-2] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] [Benchmark] scPRINT-2: Towards the Next Generation of Cell Foundation Models and Benchmarks (bioRxiv 2025) [preprint] [code]
[Pertpy] [Perturbation] [Tool] Pertpy: an End-to-end Framework for Perturbation Analysis (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Benchmarking] [Benchmark] [Perturbation] [Foundation Model] Benchmarking Algorithms for Generalizable Single-Cell Perturbation Response Prediction (Nature Methods 2025) [paper] [code] [ask deepwiki]
[DeepSpot2Cell] [Spatial] [Morphology] [Multimodal] DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision (NeurIPS 2025) [paper] [code]
[Scouter] [Perturbation] Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings (Nature Computational Science 2025) [paper] [code] [reproduce]
[GPerturb] [Perturbation] GPerturb: Gaussian process modelling of single-cell perturbation data (Nature Communications 2025) [paper] [code]
[Squidiff] [Perturbation] [Dynamics] Squidiff: Predicting Cellular Development and Responses to Perturbations using a Diffusion Model (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Nicheformer] [Foundation Model] [Spatial] [Representation Learning] Nicheformer: A Foundation Model for Single-Cell and Spatial Omics (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[NicheCompass] [Spatial] [Gene Regulation] [Representation Learning] Quantitative characterization of cell niches in spatially resolved omics data (Nature Genetics 2025) [paper] [code]
[STAMP] [Dataset] [Multimodal] [Protein] [Morphology] STAMP: Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper] [code]
[Perturb-FISH] [Perturbation] [Spatial] [Dataset] Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits (Cell 2025) [paper] [code]
[ADLF] [Perturbation] [Intervention Design] Active Learning Framework Leveraging Transcriptomics Identifies Modulators of Disease Phenotypes (Science 2025) [paper] [code] [software archive]
[Tahoe-x1] [Foundation Model] [Perturbation] [Representation Learning] Tahoe-x1: Scaling Perturbation-Trained Single-Cell Foundation Models to 3 Billion Parameters (bioRxiv 2025) [preprint] [code] [ask deepwiki] [hugging face files]
[LPM] [Foundation Model] [Perturbation] [Multimodal] In Silico Biological Discovery with Large Perturbation Models (Nature Computational Science 2025) [paper] [code] [中文解读] [ask deepwiki]
[CellNavi] [Perturbation] [Intervention Design] [Gene Regulation] CellNavi Predicts Genes Directing Cellular Transitions by Learning a Gene Graph-Enhanced Cell State Manifold (Nature Cell Biology 2025) [paper] [code] [中文解读] [ask deepwiki]
[EpiAgent] [Foundation Model] [Representation Learning] [Gene Regulation] EpiAgent: Foundation Model for Single-Cell Epigenomics (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[CellWhisperer] [Foundation Model] [Multimodal] [Representation Learning] [Tool] Multimodal learning enables chat-based exploration of single-cell data (Nature Biotechnology 2025) [paper] [code]
[CRISPR-GPT] [Agent] [Perturbation] [Intervention Design] [Tool] CRISPR-GPT for Agentic Automation of Gene-Editing Experiments (Nature Biomedical Engineering 2025) [paper] [code] [中文解读] [ask deepwiki]
[Systema] [Benchmark] [Perturbation] [Evaluation & Measurement] Systema: A Framework for Evaluating Genetic Perturbation Response Prediction Beyond Systematic Variation (Nature Biotechnology 2025) [paper] [code] [中文解读] [ask deepwiki]
[IMPA] [Perturbation] [Morphology] Predicting cell morphological responses to perturbations using generative modeling (Nature Communications 2025) [paper] [code]
[PhenoProfiler] [Morphology] [Representation Learning] PhenoProfiler: Advancing Morphology Representations for Image-based Drug Discovery (Nature Communications 2025) [paper] [code] [webserver] [ask deepwiki]
[PERISCOPE] [Morphology] [Perturbation] [Dataset] A genome-wide atlas of human cell morphology (Nature Methods 2025) [paper] [code] [dataset]
[Morph Map] [Morphology] [Perturbation] [Dataset] Morphological map of under- and overexpression of genes in human cells (Nature Methods 2025) [paper] [code] [dataset]
[MorphDiff] [Perturbation] [Morphology] [Multimodal] Prediction of Cellular Morphology Changes under Perturbations with a Transcriptome-Guided Diffusion Model (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[rBio-1] [Agent] [World Model] [Perturbation] rBio1-Training Scientific Reasoning LLMs with Biological World Models as Soft Verifiers (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[Scvi-hub] [Tool] [Dataset] Scvi-hub: An Actionable Repository for Model-Driven Single-Cell Analysis (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[GraphVelo] [Dynamics] [Multimodal] [Gene Regulation] GraphVelo Allows for Accurate Inference of Multimodal Velocities and Molecular Mechanisms for Single Cells (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[Stereo-Cell] [Spatial] [Dataset] [Multimodal] Stereo-Cell: Spatial Enhanced-Resolution Single-Cell Sequencing with High-Density DNA Nanoball-Patterned Arrays (Science 2025) [paper] [code] [中文解读] [ask deepwiki]
[SToFM] [Spatial] [Foundation Model] [Representation Learning] SToFM: A Multi-scale Foundation Model for Spatial Transcriptomics (ICML 2025) [paper] [code] [中文解读] [ask deepwiki]
[NicheFlow] [Spatial] [Dynamics] Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow (NeurIPS 2025) [paper] [code]
[scGPT-spatial] [Foundation Model] [Spatial] [Representation Learning] scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics (bioRxiv 2025) [preprint] [code]
[SpatialAgent] [Agent] [Spatial] [Multimodal] [Tool] SpatialAgent: An Autonomous AI Agent for Spatial Biology (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[CellFlux] [Perturbation] [Morphology] CellFlux: Simulating Cellular Morphology Changes via Flow Matching (ICML 2025) [paper] [code] [ask deepwiki]
[CellCLIP] [Morphology] [Perturbation] [Multimodal] [Representation Learning] CellCLIP: Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning (NeurIPS 2025) [paper] [code]
[CELTIC] [Morphology] [Protein] Cell context-dependent in silico organelle localization in label-free microscopy images (Nature Methods 2025) [paper] [code]
[MorphoDiff] [Perturbation] [Morphology] MorphoDiff: Cellular Morphology Painting with Diffusion Models (ICLR 2025) [paper] [preprint] [code]
[PRESCRIBE] [Perturbation] PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation (NeurIPS 2025) [paper] [code]
[GDE] [Representation Learning] [Morphology] [Related] Generative Distribution Embeddings: Lifting Autoencoders to the Space of Distributions for Multiscale Representation Learning (NeurIPS 2025) [paper] [preprint] [code]
[CellPB] [Benchmark] [Perturbation] Benchmarking AI Models for in Silico Gene Perturbation of Cells (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[PerturBench] [Benchmark] [Perturbation] [Tool] Benchmarking Machine Learning Models for Cellular Perturbation Analysis (NeurIPS 2025) [paper] [code]
[CellForge] [Agent] [Virtual Cell] [Perturbation] CellForge: Agentic Design of Virtual Cell Models (arXiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[Cradle-VAE] [Perturbation] [Representation Learning] Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement (AAAI 2025) [paper] [code]
[XTransferCDR] [Perturbation] [Representation Learning] Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses (AAAI 2025) [paper] [code]
[Linear Perturbation Baselines] [Benchmark] [Perturbation] [Foundation Model] [Evaluation & Measurement] Deep-Learning-Based Gene Perturbation Effect Prediction Does Not Yet Outperform Simple Linear Baselines (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Drifting Islands] [Benchmark] [Representation Learning] [Evaluation & Measurement] Limitations of Cell Embedding Metrics Assessed Using Drifting Islands (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]
[GeneAgent] [Agent] [Gene Regulation] [Tool] GeneAgent: Self-Verification Language Agent for Gene-Set Analysis Using Domain Databases (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Theory] [Virtual Cell] [Review] [Tool] [Dynamics] Human Interpretable Grammar Encodes Multicellular Systems Biology Models to Democratize Virtual Cell Laboratories (Cell 2025) [paper] [code] [ask deepwiki]
[GREmLN] [Foundation Model] [Gene Regulation] [Representation Learning] GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[CausCell] [Perturbation] [Representation Learning] [Dynamics] Causal Disentanglement for Single-Cell Representations and Controllable Counterfactual Generation (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[CLIP^n] [Morphology] [Perturbation] [Representation Learning] Transitive Prediction of Small-Molecule Function through Alignment of High-Content Screening Resources (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]
[DrugPT] [Perturbation] [Multimodal] DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling (bioRxiv 2025) [preprint]
[OmniPert] [Foundation Model] [Perturbation] OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells (bioRxiv 2025) [preprint]
[UNAGI] [Dynamics] [Perturbation] [Intervention Design] A Deep Generative Model for Deciphering Cellular Dynamics and in Silico Drug Discovery in Complex Diseases (Nature Biomedical Engineering 2025) [paper] [code] [ask deepwiki]
[OmiCLIP] [Foundation Model] [Spatial] [Morphology] [Multimodal] A Visual-Omics Foundation Model to Bridge Histopathology with Spatial Transcriptomics (Nature Methods 2025) [paper] [code] [ask deepwiki]
[OCTO-vc] [Virtual Cell] [Spatial] [Perturbation] [Morphology] OCTO-vc: Virtual Cells in Real Tissue (© by Noetik 2025) [technical report] [online demonstration]
[UniCure] [Foundation Model] [Perturbation] [Multimodal] [Intervention Design] Unicure: A Foundation Model for Predicting Personalized Cancer Therapy Response (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Cell-GraphCompass] [Foundation Model] [Gene Regulation] [Representation Learning] Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model (National Science Review 2025) [paper] [code] [ask deepwiki]
[scPRINT] [Foundation Model] [Gene Regulation] [Representation Learning] scPRINT: Pre-training on 50 Million Cells Allows Robust Gene Network Predictions (Nature Communications 2025) [paper] [code] [ask deepwiki]
[CellFM] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] CellFM: A Large-Scale Foundation Model Pre-trained on Transcriptomics of 100 Million Human Cells (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[C2S-Scale] [Foundation Model] [Multimodal] [Representation Learning] [Perturbation] C2S-Scale: Scaling Large Language Models for Next-Generation Single-Cell Analysis (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[scNET] [Representation Learning] [Gene Regulation] scNET: Learning Context-Specific Gene and Cell Embeddings by Integrating Single-Cell Gene Expression Data with Protein-Protein Interactions (Nature Methods 2025) [paper] [code] [ask deepwiki]
[ProCyon] [Foundation Model] [Protein] [Multimodal] ProCyon: A multimodal foundation model for protein phenotypes (bioRxiv 2025) [preprint] [code] [project]
[SubCell] [Foundation Model] [Morphology] [Protein] [Representation Learning] SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Token-Mol 1.0] [Related] [Multimodal] Token-Mol 1.0: Tokenized Drug Design with Large Language Models (Nature Communications 2025) [paper] [code] [ask deepwiki]
[Comment] [Virtual Cell] [Review] [Intervention Design] Virtual Cells for Predictive Immunotherapy (Nature Biotechnology Comment 2025) [paper]
[Recursion] [Virtual Cell] [Review] [Perturbation] [Intervention Design] Virtual Cells: Predict, Explain, Discover (arXiv 2025) [preprint]
[scTranslator] [Foundation Model] [Protein] [Multimodal] [Perturbation] A pre-trained large generative model for translating single-cell transcriptomes to proteomes (Nature Biomedical Engineering 2025) [paper] [preprint] [code]
[MTIProteinImputation] [Protein] [Spatial] [Morphology] Imputing single-cell protein abundance in multiplex tissue imaging (Nature Communications 2025) [paper] [code]
[Cell Maps] [Virtual Cell] [Multimodal] [Protein] [Morphology] [Dataset] Multimodal cell maps as a foundation for structural and functional genomics (Nature 2025) [paper] [code] [project]
[CellFlow] [Perturbation] [Multimodal] CellFlow Enables Generative Single-Cell Phenotype Modeling with Flow Matching (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Prophet] [Foundation Model] [Perturbation] [Multimodal] Scalable and Universal Prediction of Cellular Phenotypes (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Ovarian Co-Culture Features] [Morphology] [Perturbation] [Benchmark] Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks (Communications Biology 2025) [paper] [code]
[Grow AI Virtual Cells] [Virtual Cell] [Review] Grow AI Virtual Cells: Three Data Pillars and Closed-Loop Learning (Cell Research 2025) [paper] [中文解读]
[Build the Virtual Cell] [Virtual Cell] [Review] Build the Virtual Cell with Artificial Intelligence: A Perspective for Cancer Research (Military Medical Research 2025) [paper]
[PS] [Perturbation] [Tool] Decoding Heterogeneous Single-Cell Perturbation Responses (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]
[Mixscale] [Perturbation] [Gene Regulation] [Multimodal] [Dataset] Systematic Reconstruction of Molecular Pathway Signatures Using Scalable Single-Cell Perturbation Screens (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]
[scDrugMap] [Benchmark] [Foundation Model] [Perturbation] [Tool] scDrugMap: benchmarking large foundation models for drug response prediction (Nature Communications 2025) [paper] [code]
[VCC Commentary] [Virtual Cell] [Benchmark] [Perturbation] [Review] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper] [homepage] [beginner's guidance]
[CZI Evaluation] [Benchmark] [Virtual Cell] [Review] [Evaluation & Measurement] Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop (arXiv 2025) [preprint] [中文解读]
[Virtual Organs] [Virtual Cell] [Benchmark] [Review] [Related] From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models (iCell 2025) [paper]
[GET] [Foundation Model] [Gene Regulation] [Multimodal] A Foundation Model of Transcription across Human Cell Types (Nature 2025) [paper] [code] [ask deepwiki]

🗓️ 2024 — 18 papers

[Zero-Shot Perturbation] [Foundation Model] [Perturbation] Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction (arXiv 2024) [preprint]
[TranSiGen] [Perturbation] [Representation Learning] [Intervention Design] Deep Representation Learning of Chemical-Induced Transcriptional Profile for Phenotype-Based Drug Discovery (Nature Communications 2024) [paper] [code] [ask deepwiki]
[PRnet] [Perturbation] [Intervention Design] Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery (Nature Communications 2024) [paper] [code]
[GenePT] [Foundation Model] [Representation Learning] Simple and Effective Embedding Model for Single-Cell Biology Built from ChatGPT (Nature Biomedical Engineering 2024) [paper] [code] [ask deepwiki]
[SCimilarity] [Foundation Model] [Representation Learning] [Tool] A Cell Atlas Foundation Model for Scalable Search of Similar Human Cells (Nature 2024) [paper] [code] [ask deepwiki]
[scFoundation] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] Large-Scale Foundation Model on Single-Cell Transcriptomics (Nature Methods 2024) [paper] [code] [ask deepwiki]
[scGPT] [Foundation Model] [Representation Learning] [Multimodal] [Perturbation] [Gene Regulation] scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI (Nature Methods 2024) [paper] [code] [ask deepwiki]
[TamGen] [Related] [Multimodal] TamGen: Drug Design with Target-Aware Molecule Generation through a Chemical Language Model (Nature Communications 2024) [paper] [code] [ask deepwiki] [publication code archive]
[GeneCompass] [Foundation Model] [Representation Learning] [Gene Regulation] [Perturbation] GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with a Knowledge-Informed Cross-Species Foundation Model (Cell Research 2024) [paper] [code] [ask deepwiki]
[scTab] [Foundation Model] [Representation Learning] scTab: Scaling Cross-Tissue Single-Cell Annotation Models (Nature Communications 2024) [paper] [code] [ask deepwiki]
[SATURN] [Foundation Model] [Representation Learning] Toward Universal Cell Embeddings: Integrating Single-Cell RNA-Seq Datasets across Species with SATURN (Nature Methods 2024) [paper] [code] [ask deepwiki]
[Cell2Sentence] [Foundation Model] [Multimodal] [Representation Learning] Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024) [paper] [code] [ask deepwiki]
[LangCell] [Foundation Model] [Multimodal] [Representation Learning] LangCell: Language-Cell Pre-training for Cell Identity Understanding (ICML 2024) [paper] [code] [ask deepwiki]
[CellPLM] [Foundation Model] [Representation Learning] [Spatial] CellPLM: Pre-training of Cell Language Model beyond Single Cells (ICLR 2024) [paper] [code] [ask deepwiki]
[scPROTEIN] [Protein] [Representation Learning] scPROTEIN: A Versatile Deep Graph Contrastive Learning Framework for Single-Cell Proteomics Embedding (Nature Methods 2024) [paper] [code]
[scLinear] [Protein] [Multimodal] scLinear Predicts Protein Abundance at Single-Cell Resolution (Communications Biology 2024) [paper] [code]
[Perturbation Proteomics] [Protein] [Perturbation] [Review] AI-Empowered Perturbation Proteomics for Complex Biological Systems (Cell Genomics 2024) [paper]
[Stanford PhD Thesis] [Virtual Cell] [Perturbation] [Intervention Design] [Review] Engineering Cells Using Artificial Intelligence (© by Yusuf Roohani 2024) [paper] [GitHub Homepage] [Arc profile]

📰 Latest Updates

Deadlines for the live competitions first, then a dated log of what changed in this list. Competition entries move into the log once they close.

Live challenges (dates as published by the organizers, checked on 21 August 2026)

Challenge	Phase now	Next milestone	Final call
Virtual Embryo Challenge (NeurIPS 2026)	P2 open: validation submissions scored and ranked, starter kit and reference baselines released 15 Aug 2026	Test phase opens 20 Oct 2026	Submissions close 2 Dec 2026, winners announced 11 Dec 2026
Virtual Cell Challenge 2026 (Arc Institute)	Validation phase open: leaderboard live since 20 Aug 2026	Final test set released 22 Oct 2026	Final submissions due 5 Nov 2026, 23:59 UTC; winners announced mid to late Nov 2026 (checked 1 Oct 2026)

Updates

2026-10-02 Expanded the structured catalog from 285 to 327 papers with 42 source-checked additions, covering late-September preprints, missed 2026 peer-reviewed methods, and foundation-model/perturbation evaluation work; refreshed the English and Chinese Start Here recommendations with seven question-led tracks and explicit preprint/manuscript labels; synchronized the catalog, landscape, CSV, and BibTeX exports.
2026-10-01 Added two perturbation atlases to Datasets: genome-scale Perturb-seq in primary human CD4+ T cells (Cell 2026) and the HepG2/Jurkat essential-gene screens with TRADE (Nature Genetics 2025); updated the Virtual Cell Challenge 2026 dates from the organizers' page.
2026-09-26 Added Evaluation & Measurement as a distinct branch; reclassified metric/measurement/benchmark-validity studies and added PertResolve, Signal–Bounds–Baselines, metric failure-mode analysis, and in-the-wild VCBench.
2026-09-26 Expanded Intervention Design with VCDesign, PDGrapher, PAIRING, PerturbNet, ARC, and NUDGE; CellNavi remains a core existing entry.
2026-09-26 Restored the full Research Papers list; reviewed all 275 records for multi-label topics and repository correspondence, with source-linked curation notes.
2026-09-25 September literature sweep: added Speciesformer, PHAROS, LucaCell, DeepSCENIC, scKITE, scRep, AnnFlux, new Cell world-model papers, the Virtual Cell Challenge 2026 paper, and the TIPS review; upgraded ProteinTalks to Nature 2026 and STATE, Tahoe-100M, and scBaseCount to their Cell 2026 publications.
2026-08-21 Added the Virtual Embryo Challenge (NeurIPS 2026), grouped both competitions under Challenges and Competitions, and started this News log.
2026-08-17 Added World Model and JEPA papers, refreshed preprint links, corrected stale venue labels.
2026-08-10 Added the Virtual Cell Challenge section covering both Arc editions.
2026-08-09 Added single-cell and protein papers, including scTranslator, CAPTAIN, and scPROTEIN.
2026-07-16 Added TCGA and HEST Xenium virtual spatial transcriptomics datasets (community PR).

Earlier updates

2026-07-14 Added DeepSpot2Cell (NeurIPS 2025) (community PR).
2026-07-06 Added 27 new 2026 papers from a May to July literature sweep.
2026-07-04 Added DeSCOPE (community PR).
2026-06-22 Added 2026 papers and missing code links.
2026-05-28 Expanded the Datasets section and added recent virtual cell papers.
2026-05-13 Added the automated literature update workflow, now manual dispatch only.
2026-03-18 Restructured the README, added the contributing guide, and linked the scientific figures section.

About, data, and maintenance

Scope
Inclusion Rules
Repository & Data
Contributing

📚 Overview Papers

Five high-signal overview and perspective papers are shown by default. Expand the rest only if you want broader background coverage.

[Nature Review] Revisiting the blueprint for an interpretable virtual cell (Nature Reviews Genetics 2026) [paper]
[Cell Review] A world model of the virtual cell (Cell 2026) [paper]
[Nature Perspective] Towards Multimodal Foundation Models in Molecular Cell Biology (Nature 2025) [paper] [中文解读]
[Nature Methods] The virtual cell (Nature Methods 2025) [paper]
[Cell Perspective] How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities (Cell 2024) [paper] [中文解读]

More overview and perspective papers (13)

[Nature News] Can AI Build a Virtual Cell? Scientists Race to Model Life's Smallest Unit (Nature 2025) [paper] [中文解读]
[Nature] The Human Cell Atlas from a cell census to a unified foundation model (Nature 2024) [paper]
[Nature Review] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]
[npj Digital Medicine] AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential (npj Digital Medicine 2025) [paper]
[Nature Review] Adapting systems biology to address the complexity of human disease in the single-cell era (Nature Reviews Genetics 2025) [paper]
[Nature Genetics] Causal machine learning for single-cell genomics (Nature Genetics 2025) [paper]
[Nature Methods] Multimodal foundation transformer models for multiscale genomics (Nature Methods 2025) [paper]
[Nature Methods] AI proteomics: from protein identification to virtual cells (Nature Methods 2026) [paper]
[Review] AI virtual cells for drug discovery and pharmacology (Trends in Pharmacological Sciences 2026) [paper]
[Cell Review] World models for biomedicine (Cell 2026) [paper]
[Nature Methods] Towards predictive virtual embryos with genomics and AI (Nature Methods 2026) [paper]
[Cell Perspective] Empowering Biomedical Discovery with AI Agents (Cell 2024) [paper] [中文解读]
[Cell Review] Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas (Cell 2024) [paper] [中文解读]

🗃️ Datasets

🧫 Perturbation and Cell-State Atlases

[Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [resource] [repo]
[Tahoe-100M] Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution (Cell 2026) [paper] [preprint] [code]
[X-Atlas/Orion] Genome-Wide Perturb-Seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models (bioRxiv 2025) [paper] [dataset]
[Primary CD4+ T cell Perturb-seq] Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits (Cell 2026) [paper] [dataset]
[TRADE: HepG2 and Jurkat Perturb-seq] Transcriptome-wide analysis of differential expression in perturbation atlases (Nature Genetics 2025) [paper] [dataset]

More resources (6)

[scPerturb] scPerturb: Harmonized Single-Cell Perturbation Data (Nature Methods 2024) [paper] [dataset] [code]
[CIGS] High-Throughput Profiling of Chemical-Induced Gene Expression across 93,644 Perturbations (Nature Methods 2025) [paper] [dataset] [code]
[L1000/CMap] A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles (Cell 2017) [paper] [dataset]
[Sci-Plex] Massively Multiplex Chemical Transcriptomics at Single-Cell Resolution (Science 2019) [paper] [dataset] [code]
[Perturb-seq datasets] Genome-scale and combinatorial Perturb-seq datasets from Replogle, Norman, Dixit, Adamson, and related screens [overview]
[Virtual Cell Challenge] Community perturbation-prediction challenge and hidden evaluation resources [homepage]

🔬 Single-Cell Reference Atlases

[scBaseCount] scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository (Cell 2026) [paper] [preprint] [code-scRecounter] [code-SRAgent]
[CZ CELLxGENE Discover] A single-cell data platform for scalable exploration, analysis, and modeling of aggregated data (NAR 2025) [paper] [dataset]
[Human Cell Atlas] International atlas of human cells and tissues [portal] [paper]

More resources (3)

[Tabula Sapiens] A multiple-organ single-cell transcriptomic atlas of humans (Science 2022) [paper] [dataset]
[Human BioMolecular Atlas Program] HuBMAP healthy human tissue atlas and common coordinate framework [portal] [paper]
[GTEx] Genotype-Tissue Expression project for human tissue expression baselines [portal] [overview]

🖼️ Multimodal, Morphology, and Imaging

[scGeneScope] scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling (NeurIPS 2025 Datasets and Benchmarks Track) [paper] [dataset]
[CPJUMP1] Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations (Nature Methods 2024) [paper] [dataset] [code]
[Cell Painting Gallery] Public high-content cell painting datasets from Broad and partners [dataset] [overview]

More resources (3)

[RxRx] Recursion high-content cellular imaging datasets for perturbation and batch-correction research [datasets]
[CM4AI] Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines (bioRxiv 2024) [paper] [dataset]
[STAMP] Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper]

🗺️ Spatial and Tissue Context

[HEST-1k] HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis (NeurIPS 2024) [paper] [code]
[TCGA virtual ST atlas] TCGA virtual spatial transcriptomics atlas: DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; ~28.7k slides / 32 cancer types) (medRxiv 2026) [paper] [dataset] [code]
[HEST Xenium virtual ST] HEST Xenium virtual spatial transcriptomics: DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (medRxiv 2026) [paper] [dataset] [code]

More resources (4)

[Spatial Perturb-seq] Spatial perturb-seq data for functional genomics within intact tissue architecture (Nature Communications 2026) [paper] [code]
[Perturb-FISH] CRISPR screening with imaging-based spatial transcriptomics (Cell 2025) [paper]
[10x Genomics spatial datasets] Visium, Xenium, and related public spatial transcriptomics example datasets [datasets]
[Vizgen MERFISH datasets] Public MERFISH example datasets for spatial transcriptomics [datasets]

🧬 Protein, Organelle, and Molecular Priors

[Human Protein Atlas] Subcellular and tissue protein expression atlases [resource] [subcellular] [paper]
[OpenCell] Endogenous protein tagging, localization, and interaction data for human cellular organization (Science 2022) [paper] [dataset]
[ProtiCelli] Proteome-wide image generation resources for human cell protein localization (bioRxiv 2026) [paper] [code]

More resources (3)

[SubCell] Proteome-aware microscopy foundation model resources based on HPA images (bioRxiv 2025) [paper] [code]
[STRING] Protein functional association and interaction networks [resource] [paper]
[OmniPath] Signaling, ligand-receptor, and causal network priors for multi-omics analysis [resource] [paper]

💊 Chemical, Drug, and Target Resources

[Drug Repurposing Hub] Curated compound library with targets, mechanisms, and clinical annotations (Nature Medicine 2017) [paper] [resource]
[DepMap/CCLE/PRISM] Cancer dependency, molecular profile, and drug-response resources for cell-line perturbation modeling [resource] [paper]
[ChEMBL] Drug-like molecules, bioactivities, targets, and assays [resource] [paper]

More resources (2)

[BindingDB] Protein-small molecule binding affinity knowledgebase [resource] [paper]
[PubChem] Compound identifiers, structures, assays, and bioactivity records [resource] [paper]

🏆 Challenges and Competitions

Time-boxed competitions with hidden test sets and live leaderboards. Both entries below ask a version of the same question, whether a model can predict cell state under perturbation, at two different biological scales: cultured cell lines for the Arc challenge, a developing embryo for the NeurIPS one. Their current deadlines are mirrored at the top of News.

🧪 Virtual Cell Challenge (Arc Institute)

The Virtual Cell Challenge (VCC) is Arc Institute's annual AI virtual cell competition, explicitly modeled on CASP: every edition generates a fresh perturbation benchmark, keeps the test split hidden, and runs a live leaderboard. Two editions have run so far. Note that virtualcellchallenge.org now serves the current 2026 edition, so the 2025 links below point to Arc's archived posts rather than to the challenge site.

1st Edition (2025)

Question: can a computational model stand in for a real Perturb-seq experiment?
Task: predict the single-cell transcriptional response to held-out CRISPRi gene knockdowns, in a cell context deliberately chosen to sit off the training distribution of existing foundation models.
Data: a newly generated Perturb-seq dataset of about 300,000 H1 human embryonic stem cells (H1 hESC) covering 300 genetic perturbations, produced with 10x Genomics Flex chemistry and released in segments for training, validation, and testing.
Metrics: Perturbation Discrimination Score (PDS), Differential Expression Score (DES), and Mean Absolute Error (MAE), implemented in Arc's cell-eval suite. A Generalist Prize introduced during the competition re-ranked the 50 top-scoring final entries on the average rank across seven metrics: the three challenge metrics plus four from the STATE model paper (Pearson delta, Spearman correlation of log fold change, AUPRC, and Spearman correlation of effect size).
Timeline and scale: registration opened on 26 June 2025, winners were announced on 6 December 2025 at NeurIPS 2025. Over 5,000 people registered from 114 countries, over 1,200 teams submitted results, and over 300 teams made final submissions.
Winners:
- 1st place, $100k: BM_xTVC (BioMap Research), entry xTrimoSCPerturb, a hybrid of deep learning with classical statistical features, protein embeddings, and curated public data.
- 2nd place, $50k: XLearning Lab (Sichuan University, Tianfu Jincheng Lab), a metric-driven conditional generation framework over pseudo-bulk representations.
- 3rd place, $25k: Outlier (UChicago, Dartmouth, HKU), entry TransPert, cross-cell-line prediction from summary-level statistics with similarity-aware aggregation and global linear scaling.
- Generalist Prize, $100k: Altos Labs (Institute of Computation), entry go-with-the-flow, a flow matching generative model that learns time-dependent dynamics directly in gene expression space.
Reported takeaway: the winning entries all paired deep learning with classical statistical features. Arc's own wrap-up states that pure end-to-end neural networks did not yet outperform hybrid models. As of this entry none of the four winning entries has released code or a methods paper; the TransPert manuscript is described as forthcoming.
[Launch] Arc Institute launches its inaugural virtual cell competition (Arc Institute 2025) [announcement]
[Commentary] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper]
[Data] Behind the Data of the Virtual Cell Challenge (Arc Institute 2025) [post]
[Results] Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections (Arc Institute 2025) [post]
[Evaluation Code] cell-eval: the metrics suite used to score submissions [code]
[Metric Analysis] Effects of Distance Metrics and Scaling on the Perturbation Discrimination Score (arXiv 2025) [paper]

2nd Edition (2026)

Round two is the edition currently hosted at virtualcellchallenge.org, sponsored by NVIDIA, 10x Genomics, and Ultima Genomics. The facts below are taken from the challenge homepage as read on 17 August 2026. The scoring metrics are still unpublished, so that field stays open.

Question: does a perturbation model transfer to cell contexts in which it has never seen a perturbation?
Task: multi-context generalization and zero-shot prediction. Participants predict how multiple cell lines respond to specified gene knockdowns, given only non-targeting control profiles for those lines. Predictions are scored against new experimental perturbation data that Arc generates from those cell lines and that no submitted model has trained on.
Data: no net-new training dataset this year. Arc states participants are free to use the H1 hESC Perturb-seq data released for the 2025 edition, plus any other public or private data they have access to.
Metrics: not public. Arc states that winners will be determined by a score reflecting evaluation criteria "to be released when the Challenge is launched".
Submission policy: entries do not require open weights or code. Arc frames this as a deliberate choice to encourage industry participation alongside academia and individual entrants. It also makes this edition less reproducible than the 2025 one, so treat leaderboard positions as claims rather than as verifiable results.
Timeline: submissions and the live leaderboard open on 20 August 2026, final results are announced in late November 2026.
Prizes: the top three models win prizes valued at $100,000, $50,000, and $25,000, each half cash and half NVIDIA Brev credits.
[Homepage] Virtual Cell Challenge (Arc Institute 2026) [homepage] [Virtual Cell Initiative]

🐣 Virtual Embryo Challenge (NeurIPS 2026)

The Virtual Embryo Challenge is a NeurIPS 2026 Competition Track entry organized by the Qiu Lab at Stanford with Harvard, UC San Diego, MBZUAI, Carnegie Mellon, GenBio AI, and Vizgen. It moves the perturbation question from cultured cell lines into a developing embryo, and scores prediction across developmental time, 3D position, and gene knockout. The facts below are taken from the challenge page and the NeurIPS competition announcement as read on 21 August 2026.

Question: can a generative model predict how an embryo takes shape across space, scale, time, and perturbation?
Tasks: three, scored on shared hidden test sets. Temporal: predict gene-expression distributions at later developmental stages. Spatial-temporal: jointly predict expression and 3D location across scales, scored separately on the heart and on the whole embryo. Perturbation: predict knockout effects from wild-type data and observed perturbations.
Data: one MERFISH resource covering whole embryos and a heart-focused subset, about 1M cells across 11 time points from E6.75 to E12.5. The challenge page says "mammalian" without naming the species; the companion Virtual Embryo atlas is mouse, indexed by Theiler stage.
Tracks: two, scored separately on identical hidden test sets. The Human Team follows a conventional ML competition workflow. The Agent Team requires the run to belong to the coding agent or LLM-driven system: a human may write the initial prompt, but may not inspect intermediate results and feed judgement back in. Because both tracks are scored on the same held-out data, the agent track is a direct comparison against human-designed pipelines.
Metrics: not named on the challenge page as of 21 August 2026. The page states only that both tracks are scored on the same metrics and hidden test sets, so treat the scoring as unpublished for now.
Timeline: portal opened 10 August 2026, starter kit and reference baselines released 15 August 2026, test phase opens 20 October 2026, final submissions close 2 December 2026, winners announced 11 December 2026 at NeurIPS.
Prizes: $104K total from the Laude Institute Moonshots Seed Grant, split into $54K in winner prizes ($27K per track), $30K in travel awards, and a $20K Community Contribution Award.
[Homepage] The Virtual Embryo Challenge: Generative Modeling of Embryogenesis Across Space, Scale and Time (NeurIPS 2026 Competition Track) [homepage] [atlas] [NeurIPS announcement]
[Background] Towards Predictive Virtual Embryos with Genomics and AI (Nature Methods 2026) [paper]

📝 Reports and Blogs

[Symposium] AI Proteomics and Virtual Cell (© by Westlake University 2025) [media] [中文解读]
[Report] Projections at the Frontier: Snapshot 2025 (© by Decoding Bio's Team 2025) [slide] [中文解读]
[Post] Chan Zuckerberg Initiative's rBio Uses Virtual Cells to Train AI, Bypassing Lab Work (© by Michael Nuñez 2025) [blog]

More reports and blogs (5)

[Blog] AI's Next Frontier: Modeling Life Itself (© by Chan Zuckerberg Initiative 2025) [blog] [中文解读]
[Blog] The State of Research on Virtual Cell Modeling (© by Will Connell 2025) [blog]
[Blog] What Are Virtual Cells? Learning "Universal Representations" of Life's Fundamental Unit (© by Elliot Hershberg 2025) [blog]
[中文 Blog] 什么是虚拟细胞：AI 生物学的“登月时刻”和“苦涩教训” (© by 范阳 2025) [blog]
[Introduction] Virtual Cells (© by Udara Jay 2025) [blog]

🎥 Videos

[Arc Institute] Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE [YouTube]
[Valence Labs] Virtual Cells: Predict, Explain, Discover [YouTube]
[EPFL] Virtual Cells and Digital Twins: AI in Personalized Medicine [YouTube]

More videos (4)

[SciLifeLab] Emma Lundberg: AI Virtual Cells Could Revolutionize Biological Science [YouTube]
[Chan Zuckerberg Initiative] AI Virtual Cell Models: How AI is Accelerating Science [YouTube]
[Chan Zuckerberg Initiative] CZI's Vision for AI-Powered "Virtual Cells" [YouTube]
[Podcast] Google DeepMind CEO: We Want to Build a Virtual Cell [YouTube]

🕰️ Historical and Foundational Works

[HPA Cell Atlas] [Morphology] A subcellular map of the human proteome (Science 2017) [paper] [resource]
[OpenCell] [Morphology] OpenCell: Endogenous tagging for the cartography of human cellular organization (Science 2022) [paper] [dataset]
[Perturb-seq] [Perturbation] Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (Cell 2022) [paper] [code]
[GEARS] [Perturbation] Predicting transcriptional outcomes of novel multigene perturbations with GEARS (Nature Biotechnology 2023) [paper] [code] [ask deepwiki]
[Geneformer] [Foundation Model] Transfer Learning Enables Predictions in Network Biology (Nature 2023) [paper] [code] [ask deepwiki]
[CellOT] [Perturbation] Learning Single-Cell Perturbation Responses Using Neural Optimal Transport (Nature Methods 2023) [paper] [code] [ask deepwiki]

More historical and foundational works (14)

[tGPT] [Foundation Model] Generative Pretraining from Large-Scale Transcriptomes for Single-Cell Deciphering (iScience 2023) [paper] [code] [ask deepwiki]
[Virtual Cell] Building the Next Generation of Virtual Cells to Understand Cellular Biology (Biophysical Journal 2023) [paper]
[Research Highlight] [Virtual Cell] Simulating a Whole Cell (Nature Methods 2022) [paper]
[sciPENN] [Protein] A Multi-Use Deep Learning Method for CITE-seq and Single-Cell RNA-seq Data Integration with Cell Surface Protein Prediction and Imputation (Nature Machine Intelligence 2022) [paper] [code]
[totalVI] [Protein] Joint Probabilistic Modeling of Single-Cell Multi-Omic Data with totalVI (Nature Methods 2021) [paper] [code]
[cTP-net] [Protein] Surface Protein Imputation from Single Cell Transcriptomes by Deep Neural Networks (Nature Communications 2020) [paper] [code]
[CITE-seq] [Protein] Simultaneous Epitope and Transcriptome Measurement in Single Cells (Nature Methods 2017) [paper]
[Comment] Personalized Medicine: Time for One-Person Trials (Nature Comment 2015) [paper]
[Theory] [Virtual Cell] A Whole-Cell Computational Model Predicts Phenotype from Genotype (Cell 2012) [paper]
[Virtual Cell] The Virtual Cell - A Candidate Co-Ordinator for "Middle-Out" Modelling of Biological Systems (BIB 2009) [paper]
[VCell 7.7] [Virtual Cell] Virtual Cell Modelling and Simulation Software Environment (IET Systems Biology 2008) [paper] [software]
Quantitative Cell Biology with the Virtual Cell (Trends in Cell Biology 2003) [paper]
[Review] The Virtual Cell: A Software Environment for Computational Cell Biology (Trends in Biotechnology 2001) [paper]
[Opinion] Whole-Cell Simulation: A Grand Challenge of the 21st Century (Trends in Biotechnology 2001) [paper]

[Virtual Cell Challenge] Official challenge site for evaluation and community updates [homepage]
[Virtual Embryo] Interactive mouse development atlas with 3D reconstructions, annotated sections, and open REST and MCP endpoints [site] [challenge]
[Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [repo]
[VCell Software] Long-running software environment for computational cell biology [site]
[Noetik OCTO-vc] Technical report and demo for virtual cells in tissue [report] [demo]

🎯 Scope

Here, AIVC stands for Artificial Intelligence Virtual Cell, a term popularized by the Cell perspective "How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities." The repository is intentionally broad but practical: resources that help researchers understand, model, benchmark, or build virtual cells and related cellular foundation models.

In scope: virtual cell perspectives, perturbation modeling, single-cell and multimodal foundation models, spatial and morphology modeling, biological AI agents, datasets, benchmarks, and community resources closely connected to virtual cell research.
Also included: adjacent work that is broadly useful for the virtual cell community, especially when it contributes data, evaluation methods, or modeling tools for cellular systems.
Usually out of scope: generic biomedical AI work with weak cell-modeling relevance, low-confidence secondary sources, broken links, or items that do not add clear value beyond more central references already listed here.

✅ Inclusion Rules

Prefer peer-reviewed papers, high-signal preprints, official project pages, and primary-source links.
Include code, datasets, project pages, or Chinese summaries when they are clearly useful.
Keep entries concise and broadly reusable for readers who are scanning the field.
Use the controlled multi-label taxonomy; assign tags from the actual task and evidence, not only model names.

🧰 Repository & Data

These links are mainly for reuse, contribution, and maintenance rather than day-to-day browsing.

Searchable catalog — filter papers by keyword, year, topic, and publication status.
Curation record — per-paper tagging rationale, source links and code-association checks.
Structured paper data — machine-readable JSON backing the Research Papers section.
Architecture — how structured data, generated views, validation, and automation fit together.
Automation — how literature updates are proposed and validated.

🤝 Contributing

If you want to suggest a paper, dataset, benchmark, blog, or project, open an Issue or Pull Request. Please follow CONTRIBUTING.md for the submission format and quality bar.

⭐ Star History

aivc

awesome

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drug-discovery

foundation-models

multimodal-learning

single-cell

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Boom5426/Awesome-Virtual-Cell

Awesome-AI-Virtual-Cell: papers, datasets, benchmarks, talks, and community resources for AIVC

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updated Oct 2, 2026

boom5426.github.io/Awesome-Virtual-Cell

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README

Awesome Virtual Cell

English · 简体中文

A curated gateway to papers, datasets, benchmarks, and community resources for AI-powered virtual cell research.

Virtual Cells · Perturbation · Intervention Design · Foundation Models · Spatial

_{Start Here ·
Latest Updates ·
Papers ·
Datasets ·
Challenges}

🚀 Start Here

New to virtual cell research? Pick a track instead of reading the full list from top to bottom.

Start with the bold entry in your track, then compare the other examples. These are selective reading suggestions, not a leaderboard; preprints and unpublished manuscripts are marked explicitly.

Goal	Recommended entry points
🧬 Understand the field	Cell perspective · Grow AI Virtual Cells · Nature perspective
🧫 Predict perturbations	GEARS · STATE · MAP · CellFlow (preprint)
🧠 Cellular foundation models	Geneformer · scGPT · scFoundation · UCE · TranscriptFormer
🌐 World models & cell-state transitions	A world model of the virtual cell · CellOS (preprint) · Chreode (preprint)
🖼️ Multimodal & spatial	Nicheformer · VirTues · DePass · UniPert-G2CP
🎯 Intervention design	PDGrapher · DrugReflector · CellNavi · PAIRING · VCDesign (manuscript)
📏 Evaluation & measurement	Systema · SCMBench · PertResolve (manuscript) · Signal, Bounds & Baselines (preprint) · Principled Evaluation (preprint)

🔬 Research Papers

Browse the full research collection below; each paper may carry multiple independently assigned tags.

327 papers · 2026 (222) · 2025 (87) · 2024 (18)

Papers can have multiple topics. Search and combine topics · Tag definitions

🗓️ 2026 — 222 papers

[Recoverable Resolution] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] The recoverable resolution of cellular perturbation-response prediction (bioRxiv 2026) [preprint] [code] [source data]
[Pop-Corn] [Perturbation] [Spatial] [Intervention Design] Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts (bioRxiv 2026) [preprint]
[NexuST] [Foundation Model] [Spatial] [Representation Learning] NexuST: A Hierarchical Foundation Model for Spatial Transcriptomics (bioRxiv 2026) [preprint]
[EpiZoo] [Foundation Model] [Gene Regulation] [Multimodal] [Representation Learning] EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics (bioRxiv 2026) [preprint]
[PerturbBridge] [Perturbation] [Dynamics] [Representation Learning] PerturbBridge: Conditional Latent Schrödinger Bridge for Single-Cell Perturbation Response Prediction (bioRxiv 2026) [preprint]
[BioPert] [Perturbation] [Representation Learning] A biological-response compound representation allows chemical perturbation prediction across cell lines (bioRxiv 2026) [preprint]
[SPECTRA] [Perturbation] [Gene Regulation] SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks (bioRxiv 2026) [preprint]
[ISP³ Platform] [Tool] [Foundation Model] [Perturbation] [Intervention Design] [Dynamics] ISP³ Platform powered by Geneformer: Framework for Cross-Species, Sequential, and Multi-Gene In Silico Perturbation Screens with Application to iPS Cell State Transitions (bioRxiv 2026) [preprint]
[Aging scFM Benchmark] [Benchmark] [Foundation Model] [Evaluation & Measurement] [Related] Benchmarking single-cell foundation models for aging biology (bioRxiv 2026) [preprint]
[CellRFT] [Perturbation] [Evaluation & Measurement] CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling (arXiv 2026) [preprint]
[PopPert] [Perturbation] PopPert: Population-level Joint-Distribution Modeling for Single-Cell Perturbation Prediction (arXiv 2026) [preprint] [code]
[STP-BENCH] [Benchmark] [Spatial] [Morphology] [Evaluation & Measurement] STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images (arXiv 2026) [preprint] [code]
[RAGCell] [Foundation Model] [Multimodal] [Representation Learning] RAGCell: Retrieval-Augmented Generation as Supervision for Versatile Single-cell Analysis (arXiv 2026) [preprint]
[stFormer] [Foundation Model] [Spatial] [Gene Regulation] [Representation Learning] stFormer integrates spatial ligand signaling into a foundation model for spatial transcriptomics (Cell Reports Methods 2026) [paper]
[scLDM] [Perturbation] [Dynamics] scLDM: a conditional diffusion framework for single-cell perturbation prediction (Bioinformatics 2026) [paper] [code]
[CSGDA] [Perturbation] [Representation Learning] CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction (Bioinformatics 2026) [paper] [preprint]
[DePass] [Tool] [Spatial] [Multimodal] [Representation Learning] The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics (Nature Cell Biology 2026) [paper] [code] [documentation]
[IRIS] [Perturbation] [Dynamics] [Intervention Design] [Dataset] Reconstructing signaling histories of single cells via perturbation screens and transfer learning (Nature Methods 2026) [paper] [dataset]
[MAP] [Perturbation] [Foundation Model] [Multimodal] [Representation Learning] A knowledge-driven framework for predicting single-cell responses for unprofiled drugs (Nature Machine Intelligence 2026) [paper] [preprint] [code] [dataset]
[SCMBench] [Benchmark] [Foundation Model] [Multimodal] [Evaluation & Measurement] SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration (Nature Communications 2026) [paper] [code]
[GeneformerV2] [Foundation Model] [Representation Learning] [Gene Regulation] [Perturbation] [Tool] Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology (Nature Computational Science 2026) [paper] [code] [dataset] [documentation]
[CRISP] [Foundation Model] [Perturbation] Predicting drug responses of unseen cell types through transfer learning with foundation models (Nature Computational Science 2026) [paper] [code]
[XPert] [Perturbation] [Dynamics] [Gene Regulation] Modelling drug-induced cellular perturbation responses with a biologically informed dual-branch transformer (Nature Machine Intelligence 2026) [paper] [code] [dataset]
[Trustworthy Virtual Cells] [Virtual Cell] [Review] [Evaluation & Measurement] [Perturbation] Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models (Frontiers in Cell and Developmental Biology 2026) [paper]
[scDMC] [Foundation Model] [Representation Learning] [Gene Regulation] scDMC: Unlocking biological insight from single-cell data with an interpretable dual-stream foundation model (Genome Biology 2026) [paper]
[CellVQ] [Foundation Model] [Representation Learning] CellVQ: Illuminating cell states by a comprehensive and interpretable single cell foundation model (Nature Communications 2026) [paper]
[SpatialFormer] [Foundation Model] [Spatial] [Multimodal] [Representation Learning] SpatialFormer: universal spatial representation learning from subcellular molecular to multicellular landscapes (Nature Computational Science 2026) [paper]
[VirTues] [Virtual Cell] [Foundation Model] [Spatial] [Protein] [Representation Learning] The Virtual Tissues foundation model resolves spatial proteomics across scales (Nature 2026) [paper]
[HEX] [Spatial] [Morphology] [Protein] [Multimodal] AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer (Nature Medicine 2026) [paper]
[spEMO] [Foundation Model] [Spatial] [Morphology] [Multimodal] [Representation Learning] Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology Data (Nature Biomedical Engineering 2026) [paper]
[Scaling Is Much Pain] [Foundation Model] [Benchmark] [Evaluation & Measurement] Scaling up training dataset size for transcriptomic AI models is much pain with little gain (Nature Methods 2026) [paper]
[Biomedical FM Benchmark] [Foundation Model] [Benchmark] [Evaluation & Measurement] [Related] Benchmarking biomedical foundation models (Nature Methods 2026) [paper]
[scTranslation] [Benchmark] [Multimodal] [Evaluation & Measurement] scTranslation: A Comprehensive Benchmark for Single-Cell Multi-Omics Modality Translation (KDD 2026) [paper]
[Interpretation, Extrapolation & Perturbation] [Review] [Foundation Model] [Perturbation] [Gene Regulation] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]
[AI Digital Organism] [Review] [Virtual Cell] [World Model] [Related] How to build an AI-driven digital organism (Nature Medicine 2026) [paper]
[World Models for Biomedicine] [World Model] [Review] [Related] World models for biomedicine (Cell 2026) [paper]
[Fifteen Challenges] [Review] [Virtual Cell] [Related] Fifteen challenges for generative AI applications to cell biology (Cell 2026) [paper]
[Compositional Foundation Models] [Review] [Foundation Model] [Multimodal] From modality-specific to compositional foundation models for cell biology (Cell Systems 2026) [paper]
[Nuisance Robustness] [Foundation Model] [Benchmark] [Evaluation & Measurement] Robustness to nuisance perturbations enables unsupervised evaluation of single-cell foundation models (bioRxiv 2026) [preprint]
[Scaling Recipes] [Foundation Model] [Benchmark] [Evaluation & Measurement] Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold? (bioRxiv 2026) [preprint]
[Accessible scFM Deployment] [Foundation Model] [Evaluation & Measurement] [Tool] Accessible and reproducible deployment reveals the practical boundaries of single-cell foundation models (bioRxiv 2026) [preprint]
[Parameter-Free Representations] [Foundation Model] [Benchmark] [Evaluation & Measurement] [Representation Learning] [Related] Parameter-free representations outperform single-cell foundation models on downstream benchmarks (arXiv 2026) [preprint]
[PertResolve] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] [Tool] Measurement resolution constrains fine-grained perturbation prediction (Manuscript 2026) [preprint] [code] [project] [dataset]
[Signal, Bounds & Baselines] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] Signal, Bounds, and Baselines: Principles for Evaluating Virtual Cell Perturbation Models (bioRxiv 2026) [preprint] [code]
[Metric Failure Modes] [Evaluation & Measurement] [Benchmark] [Perturbation] [Virtual Cell] Evaluating Single-Cell Perturbation Response Models Is Far from Straightforward (bioRxiv 2026) [preprint]
[VCBench (In-the-Wild)] [Benchmark] [Evaluation & Measurement] [Perturbation] [Virtual Cell] Benchmarking virtual cell models for in-the-wild perturbation response (arXiv 2026) [preprint] [code] [project]
[VCDesign] [Intervention Design] [Perturbation] [Virtual Cell] VCDesign: Finite-Budget Intervention Design for Virtual Cells (Manuscript 2026) [preprint] [code] [project] [dataset]
[NUDGE] [Intervention Design] [Gene Regulation] [Dynamics] [Related] Uncovering minimal control of cell fate by natural dynamics (PNAS 2026) [paper] [code]
[Speciesformer] [Virtual Cell] [Foundation Model] [Multimodal] [Perturbation] [Representation Learning] Speciesformer learns conserved cellular states for cross-species generative virtual cell modeling (bioRxiv 2026) [preprint]
[DeepSCENIC] [Gene Regulation] [Multimodal] [Perturbation] DeepSCENIC: transfer learning from sequence-to-function models enables causal gene regulatory network inference (bioRxiv 2026) [preprint] [code]
[scKITE] [Foundation Model] [Representation Learning] Towards a knowledge-enhanced single-cell foundation model (arXiv 2026) [preprint] [code]
[PHAROS] [Perturbation] [Intervention Design] [Virtual Cell] PHAROS: turning single-cell perturbation models into target-directed drug-combination screens (bioRxiv 2026) [preprint] [code] [reproduce]
[ProteinTalks] [Virtual Cell] [Foundation Model] [Perturbation] [Protein] [Dynamics] [Intervention Design] An operational perturbation proteomics-based virtual cell model (Nature 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[Virtual Cell Challenge 2026] [Benchmark] [Perturbation] Virtual Cell Challenge 2026: Benchmarking zero-shot generalization across cellular contexts (Cell 2026) [paper] [challenge]
[LucaCell] [Foundation Model] [Representation Learning] [Multimodal] LucaCell: a sequence-centric foundation model for cross-species single-cell analysis (bioRxiv 2026) [preprint] [code]
[AnnFlux] [Perturbation] [Dynamics] AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics (bioRxiv 2026) [preprint]
[scRep] [Foundation Model] [Representation Learning] scRep: A Latent-Space Self-Distilled Foundation Model for Single-Cell Representation Learning (bioRxiv 2026) [preprint]
[Cell-o1] [Agent] [Benchmark] Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning (Bioinformatics 2026) [paper] [preprint] [code] [hugging face] [ask deepwiki]
[RegVelo] [Dynamics] [Gene Regulation] [Perturbation] [Multimodal] RegVelo: Gene-Regulatory-Informed Dynamics of Single Cells (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[TranscriptFormer] [Foundation Model] [Representation Learning] TranscriptFormer: A Generative Cell Atlas across 1.5 Billion Years of Evolution (Science 2026) [paper] [preprint] [code] [ask deepwiki]
[CellAtria] [Agent] [Tool] An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-Seq Data Analysis (npj Artificial Intelligence 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[CellVoyager] [Agent] [Benchmark] CellVoyager: AI CompBio Agent Generates New Insights by Autonomously Analyzing Biological Data (Nature Methods 2026) [paper] [preprint] [code] [中文解读] [ask deepwiki]
[Biomni] [Agent] [Related] Autonomous Biomedical Research with an Artificial Intelligence Agent (Science 2026) [paper] [preprint] [code] [ask deepwiki]
[STATE] [Perturbation] [Foundation Model] [Benchmark] Predicting cellular responses to perturbation across diverse contexts with State (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[UniPert-G2CP] [Perturbation] [Multimodal] UniPert-G2CP Bridges Genetic and Chemical Screens from Molecular Representation to Phenotype Modeling (Cell 2026) [paper] [preprint] [code] [ask deepwiki]
[Cell Shapes] [Morphology] [Protein] [Perturbation] Cell shapes decode molecular phenotypes in image-based spatial proteomics (Cell Systems 2026) [paper] [preprint] [code]
[scLong] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics (Nature Communications 2026) [paper] [preprint] [code] [ask deepwiki]
[UCE] [Foundation Model] [Representation Learning] Universal Cell Embedding Provides a Foundation Model for Cell Biology (Nature 2026) [paper] [preprint] [code] [ask deepwiki]
[CellOS] [Virtual Cell] [World Model] [JEPA] [Foundation Model] [Representation Learning] [Multimodal] [Perturbation] CellOS: Learning a World Model of Cellular State through Joint Embedding Prediction (bioRxiv 2026) [preprint]
[Chreode] [World Model] [Foundation Model] [Dynamics] [Perturbation] Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction (arXiv 2026) [preprint] [code]
[VCWM] [Virtual Cell] [World Model] [Review] A world model of the virtual cell (Cell 2026) [paper] [technical report]
[World Model Gaps] [World Model] [Virtual Cell] [Benchmark] [Evaluation & Measurement] What Makes a Virtual Cell a World Model? Three Gaps, Three Experiments, and a Roadmap (Research Square 2026) [preprint]
[Biomedical World Models] [World Model] [Review] [Related] Towards World Models in Biomedical Research (arXiv 2026) [preprint]
[CENO] [World Model] [Foundation Model] [Gene Regulation] [Intervention Design] [Related] CENO: A Genome-Scale World Model for Evolutionary Sequence Interpretation and Programmable Regulatory Design (bioRxiv 2026) [preprint] [code]
[Cell-JEPA] [JEPA] [Foundation Model] [Representation Learning] [Perturbation] Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics (arXiv 2026) [preprint]
[SpatialJEPA] [JEPA] [Spatial] [Multimodal] [Representation Learning] SpatialJEPA: JEPA-Inspired Graph-Context Distillation for Spatially Aware Multiomics Integration (bioRxiv 2026) [preprint] [code]
[BioM-JEPA] [JEPA] [Foundation Model] [Representation Learning] [Perturbation] BioM-JEPA: Joint-Embedding Prediction of Graph-Connected Gene Blocks in Single Cells (arXiv 2026) [preprint]
[CellWorld] [JEPA] [Foundation Model] [Spatial] [Representation Learning] CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models (arXiv 2026) [preprint] [code]
[TERRA] [JEPA] [Foundation Model] [Spatial] [Perturbation] [Representation Learning] Multi-Scale Modeling of Human Tissues from Spatial Transcriptomics with TERRA (bioRxiv 2026) [preprint] [code]
[Context Not Scale] [Virtual Cell] [Review] [Perturbation] Position: Virtual Cells Need Context, Not Just Scale (ICML 2026) [paper] [related implementation]
[scDifformer] [Virtual Cell] [Foundation Model] [Representation Learning] [Perturbation] scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data (Nucleic Acids Research 2026) [paper] [code] [project]
[OCellus] [Virtual Cell] [Foundation Model] [Multimodal] [Spatial] [Perturbation] OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning (bioRxiv 2026) [preprint]
[CellQ / PACE] [Virtual Cell] [Agent] [Intervention Design] [Perturbation] Virtual-cell verification enables self-auditing AI discovery for immune rejuvenation (bioRxiv 2026) [preprint]
[VCHarness] [Agent] [Virtual Cell] Harnessing AI to Build Virtual Cells (bioRxiv 2026) [preprint] [code]
[VCR-Agent] [Agent] [Virtual Cell] [Perturbation] [Gene Regulation] Towards Autonomous Mechanistic Reasoning in Virtual Cells (arXiv 2026) [preprint] [code]
[SpaCellAgent] [Agent] [Dynamics] [Spatial] [Tool] SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis (arXiv 2026) [preprint] [code]
[CellConsensus] [Agent] [Tool] [Dataset] CellConsensus: An agent-curated atlas for automatic cell typing (bioRxiv 2026) [preprint] [code]
[Literature-Authored Embeddings] [Agent] [Representation Learning] Coding agents author interpretable single-cell embedding models from the literature (bioRxiv 2026) [preprint] [code]
[LLM4Cell] [Benchmark] [Agent] [Review] LLM4Cell: Taxonomy and Evaluation of LLM and Agentic Models for Single-Cell Biology (ACL 2026) [paper]
[scBench-Long] [Benchmark] [Agent] [Multimodal] scBench-Long: Verifiable Benchmarking of Long-Horizon Single-Cell Biology (arXiv 2026) [preprint] [code]
[Score Distributions] [Benchmark] [Perturbation] [Evaluation & Measurement] Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap (arXiv 2026) [preprint]
[Projection Basis] [Benchmark] [Perturbation] [Representation Learning] [Evaluation & Measurement] The projection basis determines the information ceiling for perturbation prediction (bioRxiv 2026) [preprint]
[Harmonised FM Benchmark] [Benchmark] [Foundation Model] [Spatial] [Perturbation] Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation (arXiv 2026) [preprint] [code]
[scContam] [Benchmark] [Foundation Model] [Evaluation & Measurement] Auditing pretraining contamination in single-cell foundation model benchmarks (arXiv 2026) [preprint] [code]
[PertReason] [Benchmark] [Perturbation] [Agent] [Gene Regulation] PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects (arXiv 2026) [preprint] [dataset]
[DE Classification] [Benchmark] [Perturbation] [Evaluation & Measurement] Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models (bioRxiv 2026) [preprint]
[Principled Evaluation] [Benchmark] [Perturbation] [Evaluation & Measurement] Towards Principled Evaluation of Single-Cell Perturbation Prediction Models (bioRxiv 2026) [preprint] [code]
[Response Magnitude] [Benchmark] [Perturbation] [Foundation Model] [Evaluation & Measurement] Response Magnitude as a Dominant Signal for Held-Out CRISPRi Perturbation Effect Prediction (arXiv 2026) [preprint]
[SAFFRON] [Benchmark] [Foundation Model] [Spatial] Evaluating the ability of spatial transcriptomics foundation models to learn multi-scale spatial variation (bioRxiv 2026) [preprint] [code]
[Confound Diagnostics] [Benchmark] [Foundation Model] [Perturbation] [Tool] [Evaluation & Measurement] A confound-diagnostic toolkit for in silico perturbation with single-cell foundation models (bioRxiv 2026) [preprint] [code]
[Reliable Perturbations] [Benchmark] [Perturbation] [Evaluation & Measurement] Reliable single-cell perturbations explain and improve model performance (bioRxiv 2026) [preprint]
[Cell Line Bottleneck] [Benchmark] [Perturbation] [Evaluation & Measurement] Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale (bioRxiv 2026) [preprint] [code]
[ST Agent Benchmark] [Benchmark] [Agent] [Spatial] Mind the alignment gap: a spatial transcriptomics benchmark for scientific coding agents (bioRxiv 2026) [preprint]
[CRISPRko vs CRISPRi] [Benchmark] [Dataset] [Perturbation] Direct comparison of CRISPR knockout and interference with Perturb-seq (bioRxiv 2026) [preprint] [code]
[JUMP-lite] [Benchmark] [Morphology] [Dataset] [Tool] JUMP-lite: Compact, reproducible benchmarking of cell representations (arXiv 2026) [preprint] [code]
[scVision] [Foundation Model] [Representation Learning] A vision foundation model for single-cell biology via spatial gene cartography (arXiv 2026) [preprint] [project]
[SATScG] [Foundation Model] [Representation Learning] Scaling an Autoregressive Transformer for Single-Cell Generation (arXiv 2026) [preprint] [code]
[Gene Intelligence] [Foundation Model] [Representation Learning] [Benchmark] Raw-count embeddings improve single-cell foundation models (bioRxiv 2026) [preprint] [code]
[Task-Adapted FM] [Foundation Model] [Perturbation] [Representation Learning] Task-adapted biological foundation models uncover perturbation-centric representations (bioRxiv 2026) [preprint] [code]
[Species-Native Tokens] [Foundation Model] [Representation Learning] Single-cell foundation modeling with species-native protein tokens links regenerative competence across frog and mouse (bioRxiv 2026) [preprint] [code]
[Complementary Views] [Foundation Model] [Representation Learning] [Gene Regulation] Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views (arXiv 2026) [preprint]
[Tabula] [Foundation Model] [Gene Regulation] [Intervention Design] Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning (arXiv 2026) [preprint] [code]
[AdaGeneBudget] [Foundation Model] [Tool] [Representation Learning] AdaGeneBudget: Cell-Adaptive Gene-Token Allocation for Efficient Single-Cell Foundation Models (bioRxiv 2026) [preprint]
[CellTosg2Sequence] [Foundation Model] [Multimodal] [Representation Learning] CellTosg2Sequence: A Unified Text-Omics-Signaling-Graph Large Language Model for Single-Cell Analysis (bioRxiv 2026) [preprint]
[Stable-Shift] [Perturbation] Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations (arXiv 2026) [preprint] [code]
[PertOmni] [Perturbation] [Multimodal] [Morphology] [Representation Learning] Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings (bioRxiv 2026) [preprint]
[scCycleMol] [Perturbation] Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses (arXiv 2026) [preprint]
[GenPerturb] [Perturbation] [Gene Regulation] GenPerturb: sequence-grounded interpretation of perturbation transcriptomes using pretrained genomic models (bioRxiv 2026) [preprint] [code]
[U-Pert] [Perturbation] [Dynamics] [Intervention Design] Unbalanced Perturbation Dynamics For Cell Fate Design (bioRxiv 2026) [preprint] [code]
[GeneSpeak-FP] [Perturbation] [Intervention Design] GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures (arXiv 2026) [preprint]
[PerturbPFN] [Perturbation] [Foundation Model] [Gene Regulation] PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling (ICML 2026) [paper]
[Response Decomposition] [Perturbation] [Benchmark] Perturbation response decomposition enables biologically aligned generalization to unseen perturbations and cellular contexts (bioRxiv 2026) [preprint] [code]
[Cytokine Atlas] [Perturbation] [Gene Regulation] [Dataset] A human cytokine response atlas to reconstruct underlying gene regulatory networks (bioRxiv 2026) [preprint] [code] [project] [dataset]
[PerturbMap] [Perturbation] PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses (arXiv 2026) [preprint]
[LGR] [Perturbation] [Agent] LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses (ICLR 2026) [paper]
[MEGA-ODE] [Perturbation] [Dynamics] [Gene Regulation] [Intervention Design] MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics (bioRxiv 2026) [preprint] [code]
[TranScouter] [Perturbation] [Benchmark] A structured study of cross-condition prediction of transcriptional responses to gene perturbations (bioRxiv 2026) [preprint] [code]
[COMPASS] [Perturbation] COMPASS: Component-Wise Inference of Shared and Gene-Specific Perturbation Response (bioRxiv 2026) [preprint] [code]
[SLIM] [Perturbation] SLIM: A small linear model with STRING embeddings for single-cell genetic perturbation prediction (bioRxiv 2026) [preprint] [code]
[GeneGeoFlow] [Perturbation] Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling (arXiv 2026) [preprint]
[PerturbLDM] [Perturbation] PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responses (bioRxiv 2026) [preprint] [code]
[Coladan] [Spatial] [Morphology] [Multimodal] [Perturbation] [Dataset] Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts (Genome Medicine 2026) [paper] [code]
[Spaceland] [Spatial] [Morphology] [Multimodal] Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics (bioRxiv 2026) [preprint] [code (project)]
[TissueFormer (Pathology)] [Spatial] [Morphology] [Multimodal] [Foundation Model] Multi-Modal Foundation Model with Whole-Slide Attention Enables Transferrable Digital Pathology at Single-Cell Resolution (bioRxiv 2026) [preprint] [code]
[VOICE] [Spatial] [Morphology] [Multimodal] [Foundation Model] VOICE: A Vision-Omics Foundation Model Integrating Direct and Retrieval-Based Prediction of In-situ Single-Cell Gene Expression (arXiv 2026) [preprint] [code]
[VISTA] [Spatial] [Morphology] [Multimodal] Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer (bioRxiv 2026) [preprint] [code]
[SQUINT] [Spatial] [Representation Learning] Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization (bioRxiv 2026) [preprint] [code (project)]
[MAE-3D] [Morphology] [Multimodal] [Representation Learning] [Benchmark] 3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy (arXiv 2026) [preprint] [code]
[Cell Painting RAG Audit] [Morphology] [Agent] [Benchmark] Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology (arXiv 2026) [preprint]
[Spatium] [Protein] [Spatial] [Foundation Model] [Representation Learning] Spatium: A Protein Language Foundation Model for Spatial Proteomics (bioRxiv 2026) [preprint] [code]
[PerturbMatch] [Perturbation] [Tool] Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq (bioRxiv 2026) [preprint] [code]
[scRepresenter] [Representation Learning] [Tool] [Benchmark] [Foundation Model] scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics (bioRxiv 2026) [preprint] [code]
[CELLens] [Virtual Cell] [Gene Regulation] [Tool] Human-Guided Causal Knowledge Injection for Virtual Cells (arXiv 2026) [preprint] [code]
[Tabular FM Perturbation] [Benchmark] [Perturbation] [Foundation Model] Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales (bioRxiv 2026) [preprint] [code]
[CellFM-Datasets] [Foundation Model] [Spatial] [Tool] Cellfm-datasets: A Unified Data Infrastructure for Single-Cell and Spatial Transcriptomics Foundation Model Pretraining (bioRxiv 2026) [preprint]
[OCOO-T] [Virtual Cell] [Perturbation] OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction (arXiv 2026) [preprint]
[Glitch Genes] [Benchmark] [Foundation Model] [Representation Learning] Glitch genes: embedding geometry predicts functional fragility in single-cell foundation models (bioRxiv 2026) [preprint]
[VCBench] [Virtual Cell] [Benchmark] [Foundation Model] VCBench: A Multi-Dimensional Benchmark for Single-Cell Foundation Models (bioRxiv 2026) [preprint] [code]
[Design Space] [Perturbation] [Benchmark] [Representation Learning] Elucidating the Design Space of Generative Models for Single-Cell Perturbation Prediction (bioRxiv 2026) [preprint] [code]
[PertDiffBench] [Perturbation] [Benchmark] PertDiffBench: Benchmarking Diffusion Models for Single-Cell Perturbation Response Prediction (bioRxiv 2026) [preprint] [code (project)]
[Zero-Shot Benchmark] [Benchmark] [Foundation Model] [Representation Learning] Systematic benchmarking of zero-shot utility and robustness in single-cell transcriptomic foundation models (bioRxiv 2026) [preprint] [code]
[DeepSpot-M] [Spatial] [Morphology] [Multimodal] [Foundation Model] DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology (medRxiv 2026) [paper] [code]
[V3Cell] [Virtual Cell] [Morphology] [Perturbation] [Dynamics] V3Cell: A Vision-Guided Virtual 3D Cell Framework for Phenotypic Modeling and Perturbation Prediction (bioRxiv 2026) [preprint] [code]
[Cross-Context DrugPert] [Perturbation] [Multimodal] Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework (Bioinformatics 2026) [paper] [code]
[SciCore-Omics] [Foundation Model] [Spatial] [Morphology] [Multimodal] SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology (bioRxiv 2026) [preprint] [code]
[HoloCell] [Virtual Cell] [Foundation Model] [Multimodal] [Protein] HoloCell: A Generative Foundation Model for Holistic Cellular Modeling (bioRxiv 2026) [preprint] [project (code pending)]
[FM Roadmap] [Foundation Model] [Spatial] [Review] A User’s Roadmap to Foundation Models on Single-Cell and Spatial-Omics – Cell Type and Lineage applications (National Science Review 2026) [paper]
[PerturbCellRL] [Perturbation] PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction (arXiv 2026) [preprint]
[KG-Reasoning LLM] [Perturbation] [Agent] Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors (arXiv 2026) [preprint]
[Cross-Modal Transfer] [Foundation Model] [Multimodal] [Spatial] Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models (arXiv 2026) [preprint]
[BRIDGE] [Foundation Model] [Morphology] [Spatial] [Multimodal] BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer Diagnosis (bioRxiv 2026) [preprint] [code]
[Morphodynamics-Expr] [Morphology] [Dynamics] [Multimodal] Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change (Cell Systems 2026) [paper] [code] [implementation package]
[Morph-Transcriptomic GenModel] [Perturbation] [Morphology] [Multimodal] A generative framework for predicting cellular morphological and transcriptomic perturbation responses (Cell Reports Methods 2026) [paper] [code]
[DoFormer] [Perturbation] [Gene Regulation] [Multimodal] DoFormer: Causal Transformer for Gene Perturbation (bioRxiv 2026) [preprint]
[RegFormer] [Foundation Model] [Gene Regulation] [Representation Learning] RegFormer: a single-cell foundation model powered by gene regulatory hierarchies (Nature Communications 2026) [paper] [code]
[Spurious Correlation] [Benchmark] [Perturbation] [Evaluation & Measurement] Spurious correlation inflates performance in single-cell perturbation prediction (bioRxiv 2026) [preprint] [code]
[scArchon] [Benchmark] [Perturbation] [Tool] scArchon: a scalable benchmarking framework for assessing single-cell perturbation models (Genome Biology 2026) [paper] [code]
[Chemical Pert DL] [Benchmark] [Perturbation] [Evaluation & Measurement] Deep learning models for chemical perturbation prediction do not yet utilise drug molecular features (bioRxiv 2026) [preprint]
[Cycle-Consistent GenModel] [Spatial] [Multimodal] [Protein] [Representation Learning] Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities (bioRxiv 2026) [preprint] [code]
[StateXDiff] [Perturbation] [Multimodal] StateXDiff: Cell State-Contextualized Multimodal Diffusion for Single-Cell Perturbation Prediction (arXiv 2026) [preprint]
[DeSCOPE] [Perturbation] [Multimodal] Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE (bioRxiv 2026) [preprint] [code]
[Dataset Size & Diversity] [Foundation Model] [Benchmark] Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance (Nature Methods 2026) [paper] [code]
[Lingshu-Cell] [Virtual Cell] [World Model] [Foundation Model] [Perturbation] Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells (arXiv 2026) [preprint] [code] [homepage]
[SCALE] [Perturbation] SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction (arXiv 2026) [preprint]
[Conditional Monge Gap] [Perturbation] Conditional Monge Gap enables generalizable single-cell perturbation modelling (Nature Machine Intelligence 2026) [paper] [code]
[ProtiCelli] [Virtual Cell] [Protein] [Morphology] Generative machine learning unlocks the first proteome-wide image of human cells (bioRxiv 2026) [preprint] [code]
[AetherCell] [Virtual Cell] [Foundation Model] [Perturbation] [Intervention Design] AetherCell: A generative engine for virtual cell perturbation and in vivo drug discovery (bioRxiv 2026) [preprint] [code]
[AlphaCell] [Virtual Cell] [World Model] [Perturbation] [Dynamics] Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell (bioRxiv 2026) [preprint]
[VCWorld] [Virtual Cell] [World Model] [Foundation Model] [Perturbation] VCWorld: A Biological World Model for Virtual Cell Simulation (ICLR 2026) [paper] [code] [ask deepwiki]
[Spatial Perturb-seq] [Perturbation] [Spatial] [Dataset] Spatial perturb-seq: single-cell functional genomics within intact tissue architecture (Nature Communications 2026) [paper] [code]
[Celcomen] [Perturbation] [Spatial] [Gene Regulation] Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling (Nature Communications 2026) [paper] [code]
[stVCR] [Spatial] [Dynamics] stVCR: spatiotemporal dynamics of single cells (Nature Methods 2026) [paper] [code]
[CONCORD] [Representation Learning] Revealing a coherent cell-state landscape across single-cell datasets with CONCORD (Nature Biotechnology 2026) [paper] [code]
[AI Scientist] [Agent] [Related] Towards end-to-end automation of AI research (Nature 2026) [paper] [code] [template-free code]
[Conformation Description Language] [Protein] [Multimodal] [Related] Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language (Nature Machine Intelligence 2026) [paper] [code]
[CRISPRi Map] [Perturbation] [Gene Regulation] [Dataset] A genome-scale single-cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines (Cell Genomics 2026) [paper] [code] [publisher]
[TxPert] [Perturbation] TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects (Nature Biotechnology 2026) [paper] [code]
[Therapeutic Design] [Perturbation] [Intervention Design] Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes (Cell 2026) [paper] [code]
[X-Pert] [Perturbation] [Multimodal] Unified Multimodal Learning Enables Generalized Cellular Response Prediction to Diverse Perturbations (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[MVCBench] [Benchmark] [Perturbation] [Multimodal] [Morphology] MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[HarmonyCell] [Agent] [Perturbation] [Tool] HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts (bioRxiv 2026) [preprint]
[scDFM] [Perturbation] scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction (ICLR 2026) [paper] [code]
[Doloris] [Perturbation] Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation (ICLR 2026) [paper] [code]
[Departures] [Perturbation] Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges (AAAI 2026) [paper] [preprint] [code]
[PETRI] [Foundation Model] [Multimodal] [Representation Learning] PETRI: Learning Unified Cell Embeddings from Unpaired Modalities via Early-Fusion Joint Reconstruction (ICLR 2026) [paper]
[STRAND] [Perturbation] [Gene Regulation] STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations (arXiv 2026) [preprint]
[PerturbDiff] [Perturbation] PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling (arXiv 2026) [preprint] [code] [project]
[scBIG] [Perturbation] [Representation Learning] [Gene Regulation] Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction (arXiv 2026) [preprint] [code]
[CellxPert] [Foundation Model] [Perturbation] [Multimodal] [Spatial] [Protein] CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation (ICLR 2026) [paper]
[Perturbation Representation] [Perturbation] [Representation Learning] [Benchmark] [Evaluation & Measurement] What Makes a Representation Good for Single-Cell Perturbation Prediction? (arXiv 2026) [preprint] [code]
[CisTransCell] [Perturbation] [Gene Regulation] [Multimodal] CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context (arXiv 2026) [preprint]
[Latent Causal Processes] [Perturbation] [Dynamics] [Gene Regulation] Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction (arXiv 2026) [preprint] [code]
[msInfer] [Protein] [Multimodal] [Tool] Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer (Research Square 2026) [preprint] [code]
[Stack] [Foundation Model] [Representation Learning] [Perturbation] Stack: In-Context Learning of Single-Cell Biology (bioRxiv 2026) [preprint] [code] [ask deepwiki]
[BioWorldModel] [World Model] [Dynamics] [Related] BioWorldModel: a single architecture predicts phenotype from genotype across four kingdoms of life (bioRxiv 2026) [preprint]
[Gene Importance] [Foundation Model] [Gene Regulation] [Tool] Scoring gene importance by interpreting single-cell foundation models (Nature Biotechnology 2026) [paper] [code]
[Hi-C FM] [Foundation Model] [Gene Regulation] [Multimodal] A generalizable Hi-C foundation model for chromatin architecture, single-cell and multiomics analysis across species (Nature Methods 2026) [paper] [code]
[Virtual Spatial Tumor] [Morphology] [Spatial] [Protein] [Multimodal] Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology (Cell 2026) [paper] [code]
[SynCell] [Virtual Cell] [Review] [Related] A framework for building a synthetic cell from the SynCell Asia Initiative (Nature Biotechnology 2026) [paper]
[3D Genome FM] [Foundation Model] [Gene Regulation] [Review] A foundation model to help understand the regulatory implications of 3D genome organization (Nature Methods 2026) [paper]
[TissueFormer (Population)] [Foundation Model] [Representation Learning] Tissueformer: extending single-cell foundation models to predict population-level phenotypes (BMC Bioinformatics 2026) [paper] [code]
[CytoSignal] [Spatial] [Dynamics] [Gene Regulation] [Tool] CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data (Nature Genetics 2026) [paper] [code]
[graphene-seq] [Spatial] [Multimodal] [Dynamics] [Dataset] In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments (Nature Communications 2026) [paper] [code]
[Deep Molecular Profiling] [Spatial] [Multimodal] [Review] Deep molecular profiling in three dimensions (Nature Methods 2026) [paper]
[SpaMosaic] [Spatial] [Multimodal] [Protein] [Representation Learning] Mosaic integration of spatial multi-omics with SpaMosaic (Nature Genetics 2026) [paper] [code]
[Computational Landscape] [Perturbation] [Review] Charting the computational landscape of single-cell genetic perturbation (Journal of Advanced Research 2026) [paper]
[veloAgent] [Dynamics] [Spatial] [Intervention Design] Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent (Molecular Systems Biology 2026) [paper] [code]
[Morphodynamics] [Morphology] [Dynamics] Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation (Molecular Systems Biology 2026) [paper] [code] [image-processing code]
[Single Cell Notebooks] [Tool] [Spatial] The Single Cell Notebooks for inclusive and accessible training in single-cell and spatial omics (Nature Genetics 2026) [paper] [code]
[CAPTAIN] [Foundation Model] [Multimodal] [Protein] [Representation Learning] CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein (Nature Communications 2026) [paper] [preprint] [code]
[scpFormer] [Foundation Model] [Protein] [Representation Learning] scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics (arXiv 2026) [preprint] [code]
[MultiPert] [Perturbation] [Multimodal] [Protein] MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction (PLOS Computational Biology 2026) [paper] [code]

🗓️ 2025 — 87 papers

[PDGrapher] [Perturbation] [Intervention Design] [Gene Regulation] [Representation Learning] Combinatorial prediction of therapeutic perturbations using causally inspired neural networks (Nature Biomedical Engineering 2025) [paper] [preprint] [code] [project]
[PAIRING] [Perturbation] [Intervention Design] [Representation Learning] Identifying an optimal perturbation to induce a desired cell state by generative deep learning (Cell Systems 2025) [paper] [code]
[ARC] [Intervention Design] [Gene Regulation] [Dynamics] [Related] Reverse control of biological networks to restore phenotype landscapes (Science Advances 2025) [paper] [code] [software archive]
[PerturbNet] [Perturbation] [Intervention Design] [Representation Learning] PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations (Molecular Systems Biology 2025) [paper] [preprint] [code]
[GeneJEPA] [JEPA] [World Model] [Foundation Model] [Representation Learning] [Perturbation] GeneJEPA: A Predictive World Model of the Transcriptome (bioRxiv 2025) [preprint] [code]
[STELLA] [Agent] [Multimodal] [Related] STELLA: Towards a Biomedical World Model with Self-Evolving Multimodal Agents (bioRxiv 2025) [preprint] [code]
[scPRINT-2] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] [Benchmark] scPRINT-2: Towards the Next Generation of Cell Foundation Models and Benchmarks (bioRxiv 2025) [preprint] [code]
[Pertpy] [Perturbation] [Tool] Pertpy: an End-to-end Framework for Perturbation Analysis (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Benchmarking] [Benchmark] [Perturbation] [Foundation Model] Benchmarking Algorithms for Generalizable Single-Cell Perturbation Response Prediction (Nature Methods 2025) [paper] [code] [ask deepwiki]
[DeepSpot2Cell] [Spatial] [Morphology] [Multimodal] DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision (NeurIPS 2025) [paper] [code]
[Scouter] [Perturbation] Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings (Nature Computational Science 2025) [paper] [code] [reproduce]
[GPerturb] [Perturbation] GPerturb: Gaussian process modelling of single-cell perturbation data (Nature Communications 2025) [paper] [code]
[Squidiff] [Perturbation] [Dynamics] Squidiff: Predicting Cellular Development and Responses to Perturbations using a Diffusion Model (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Nicheformer] [Foundation Model] [Spatial] [Representation Learning] Nicheformer: A Foundation Model for Single-Cell and Spatial Omics (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[NicheCompass] [Spatial] [Gene Regulation] [Representation Learning] Quantitative characterization of cell niches in spatially resolved omics data (Nature Genetics 2025) [paper] [code]
[STAMP] [Dataset] [Multimodal] [Protein] [Morphology] STAMP: Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper] [code]
[Perturb-FISH] [Perturbation] [Spatial] [Dataset] Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits (Cell 2025) [paper] [code]
[ADLF] [Perturbation] [Intervention Design] Active Learning Framework Leveraging Transcriptomics Identifies Modulators of Disease Phenotypes (Science 2025) [paper] [code] [software archive]
[Tahoe-x1] [Foundation Model] [Perturbation] [Representation Learning] Tahoe-x1: Scaling Perturbation-Trained Single-Cell Foundation Models to 3 Billion Parameters (bioRxiv 2025) [preprint] [code] [ask deepwiki] [hugging face files]
[LPM] [Foundation Model] [Perturbation] [Multimodal] In Silico Biological Discovery with Large Perturbation Models (Nature Computational Science 2025) [paper] [code] [中文解读] [ask deepwiki]
[CellNavi] [Perturbation] [Intervention Design] [Gene Regulation] CellNavi Predicts Genes Directing Cellular Transitions by Learning a Gene Graph-Enhanced Cell State Manifold (Nature Cell Biology 2025) [paper] [code] [中文解读] [ask deepwiki]
[EpiAgent] [Foundation Model] [Representation Learning] [Gene Regulation] EpiAgent: Foundation Model for Single-Cell Epigenomics (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[CellWhisperer] [Foundation Model] [Multimodal] [Representation Learning] [Tool] Multimodal learning enables chat-based exploration of single-cell data (Nature Biotechnology 2025) [paper] [code]
[CRISPR-GPT] [Agent] [Perturbation] [Intervention Design] [Tool] CRISPR-GPT for Agentic Automation of Gene-Editing Experiments (Nature Biomedical Engineering 2025) [paper] [code] [中文解读] [ask deepwiki]
[Systema] [Benchmark] [Perturbation] [Evaluation & Measurement] Systema: A Framework for Evaluating Genetic Perturbation Response Prediction Beyond Systematic Variation (Nature Biotechnology 2025) [paper] [code] [中文解读] [ask deepwiki]
[IMPA] [Perturbation] [Morphology] Predicting cell morphological responses to perturbations using generative modeling (Nature Communications 2025) [paper] [code]
[PhenoProfiler] [Morphology] [Representation Learning] PhenoProfiler: Advancing Morphology Representations for Image-based Drug Discovery (Nature Communications 2025) [paper] [code] [webserver] [ask deepwiki]
[PERISCOPE] [Morphology] [Perturbation] [Dataset] A genome-wide atlas of human cell morphology (Nature Methods 2025) [paper] [code] [dataset]
[Morph Map] [Morphology] [Perturbation] [Dataset] Morphological map of under- and overexpression of genes in human cells (Nature Methods 2025) [paper] [code] [dataset]
[MorphDiff] [Perturbation] [Morphology] [Multimodal] Prediction of Cellular Morphology Changes under Perturbations with a Transcriptome-Guided Diffusion Model (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[rBio-1] [Agent] [World Model] [Perturbation] rBio1-Training Scientific Reasoning LLMs with Biological World Models as Soft Verifiers (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[Scvi-hub] [Tool] [Dataset] Scvi-hub: An Actionable Repository for Model-Driven Single-Cell Analysis (Nature Methods 2025) [paper] [code] [中文解读] [ask deepwiki]
[GraphVelo] [Dynamics] [Multimodal] [Gene Regulation] GraphVelo Allows for Accurate Inference of Multimodal Velocities and Molecular Mechanisms for Single Cells (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[Stereo-Cell] [Spatial] [Dataset] [Multimodal] Stereo-Cell: Spatial Enhanced-Resolution Single-Cell Sequencing with High-Density DNA Nanoball-Patterned Arrays (Science 2025) [paper] [code] [中文解读] [ask deepwiki]
[SToFM] [Spatial] [Foundation Model] [Representation Learning] SToFM: A Multi-scale Foundation Model for Spatial Transcriptomics (ICML 2025) [paper] [code] [中文解读] [ask deepwiki]
[NicheFlow] [Spatial] [Dynamics] Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow (NeurIPS 2025) [paper] [code]
[scGPT-spatial] [Foundation Model] [Spatial] [Representation Learning] scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics (bioRxiv 2025) [preprint] [code]
[SpatialAgent] [Agent] [Spatial] [Multimodal] [Tool] SpatialAgent: An Autonomous AI Agent for Spatial Biology (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[CellFlux] [Perturbation] [Morphology] CellFlux: Simulating Cellular Morphology Changes via Flow Matching (ICML 2025) [paper] [code] [ask deepwiki]
[CellCLIP] [Morphology] [Perturbation] [Multimodal] [Representation Learning] CellCLIP: Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning (NeurIPS 2025) [paper] [code]
[CELTIC] [Morphology] [Protein] Cell context-dependent in silico organelle localization in label-free microscopy images (Nature Methods 2025) [paper] [code]
[MorphoDiff] [Perturbation] [Morphology] MorphoDiff: Cellular Morphology Painting with Diffusion Models (ICLR 2025) [paper] [preprint] [code]
[PRESCRIBE] [Perturbation] PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation (NeurIPS 2025) [paper] [code]
[GDE] [Representation Learning] [Morphology] [Related] Generative Distribution Embeddings: Lifting Autoencoders to the Space of Distributions for Multiscale Representation Learning (NeurIPS 2025) [paper] [preprint] [code]
[CellPB] [Benchmark] [Perturbation] Benchmarking AI Models for in Silico Gene Perturbation of Cells (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[PerturBench] [Benchmark] [Perturbation] [Tool] Benchmarking Machine Learning Models for Cellular Perturbation Analysis (NeurIPS 2025) [paper] [code]
[CellForge] [Agent] [Virtual Cell] [Perturbation] CellForge: Agentic Design of Virtual Cell Models (arXiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[Cradle-VAE] [Perturbation] [Representation Learning] Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement (AAAI 2025) [paper] [code]
[XTransferCDR] [Perturbation] [Representation Learning] Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses (AAAI 2025) [paper] [code]
[Linear Perturbation Baselines] [Benchmark] [Perturbation] [Foundation Model] [Evaluation & Measurement] Deep-Learning-Based Gene Perturbation Effect Prediction Does Not Yet Outperform Simple Linear Baselines (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Drifting Islands] [Benchmark] [Representation Learning] [Evaluation & Measurement] Limitations of Cell Embedding Metrics Assessed Using Drifting Islands (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]
[GeneAgent] [Agent] [Gene Regulation] [Tool] GeneAgent: Self-Verification Language Agent for Gene-Set Analysis Using Domain Databases (Nature Methods 2025) [paper] [code] [ask deepwiki]
[Theory] [Virtual Cell] [Review] [Tool] [Dynamics] Human Interpretable Grammar Encodes Multicellular Systems Biology Models to Democratize Virtual Cell Laboratories (Cell 2025) [paper] [code] [ask deepwiki]
[GREmLN] [Foundation Model] [Gene Regulation] [Representation Learning] GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[CausCell] [Perturbation] [Representation Learning] [Dynamics] Causal Disentanglement for Single-Cell Representations and Controllable Counterfactual Generation (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[CLIP^n] [Morphology] [Perturbation] [Representation Learning] Transitive Prediction of Small-Molecule Function through Alignment of High-Content Screening Resources (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]
[DrugPT] [Perturbation] [Multimodal] DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling (bioRxiv 2025) [preprint]
[OmniPert] [Foundation Model] [Perturbation] OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells (bioRxiv 2025) [preprint]
[UNAGI] [Dynamics] [Perturbation] [Intervention Design] A Deep Generative Model for Deciphering Cellular Dynamics and in Silico Drug Discovery in Complex Diseases (Nature Biomedical Engineering 2025) [paper] [code] [ask deepwiki]
[OmiCLIP] [Foundation Model] [Spatial] [Morphology] [Multimodal] A Visual-Omics Foundation Model to Bridge Histopathology with Spatial Transcriptomics (Nature Methods 2025) [paper] [code] [ask deepwiki]
[OCTO-vc] [Virtual Cell] [Spatial] [Perturbation] [Morphology] OCTO-vc: Virtual Cells in Real Tissue (© by Noetik 2025) [technical report] [online demonstration]
[UniCure] [Foundation Model] [Perturbation] [Multimodal] [Intervention Design] Unicure: A Foundation Model for Predicting Personalized Cancer Therapy Response (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Cell-GraphCompass] [Foundation Model] [Gene Regulation] [Representation Learning] Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model (National Science Review 2025) [paper] [code] [ask deepwiki]
[scPRINT] [Foundation Model] [Gene Regulation] [Representation Learning] scPRINT: Pre-training on 50 Million Cells Allows Robust Gene Network Predictions (Nature Communications 2025) [paper] [code] [ask deepwiki]
[CellFM] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] CellFM: A Large-Scale Foundation Model Pre-trained on Transcriptomics of 100 Million Human Cells (Nature Communications 2025) [paper] [code] [中文解读] [ask deepwiki]
[C2S-Scale] [Foundation Model] [Multimodal] [Representation Learning] [Perturbation] C2S-Scale: Scaling Large Language Models for Next-Generation Single-Cell Analysis (bioRxiv 2025) [preprint] [code] [中文解读] [ask deepwiki]
[scNET] [Representation Learning] [Gene Regulation] scNET: Learning Context-Specific Gene and Cell Embeddings by Integrating Single-Cell Gene Expression Data with Protein-Protein Interactions (Nature Methods 2025) [paper] [code] [ask deepwiki]
[ProCyon] [Foundation Model] [Protein] [Multimodal] ProCyon: A multimodal foundation model for protein phenotypes (bioRxiv 2025) [preprint] [code] [project]
[SubCell] [Foundation Model] [Morphology] [Protein] [Representation Learning] SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Token-Mol 1.0] [Related] [Multimodal] Token-Mol 1.0: Tokenized Drug Design with Large Language Models (Nature Communications 2025) [paper] [code] [ask deepwiki]
[Comment] [Virtual Cell] [Review] [Intervention Design] Virtual Cells for Predictive Immunotherapy (Nature Biotechnology Comment 2025) [paper]
[Recursion] [Virtual Cell] [Review] [Perturbation] [Intervention Design] Virtual Cells: Predict, Explain, Discover (arXiv 2025) [preprint]
[scTranslator] [Foundation Model] [Protein] [Multimodal] [Perturbation] A pre-trained large generative model for translating single-cell transcriptomes to proteomes (Nature Biomedical Engineering 2025) [paper] [preprint] [code]
[MTIProteinImputation] [Protein] [Spatial] [Morphology] Imputing single-cell protein abundance in multiplex tissue imaging (Nature Communications 2025) [paper] [code]
[Cell Maps] [Virtual Cell] [Multimodal] [Protein] [Morphology] [Dataset] Multimodal cell maps as a foundation for structural and functional genomics (Nature 2025) [paper] [code] [project]
[CellFlow] [Perturbation] [Multimodal] CellFlow Enables Generative Single-Cell Phenotype Modeling with Flow Matching (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Prophet] [Foundation Model] [Perturbation] [Multimodal] Scalable and Universal Prediction of Cellular Phenotypes (bioRxiv 2025) [preprint] [code] [ask deepwiki]
[Ovarian Co-Culture Features] [Morphology] [Perturbation] [Benchmark] Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks (Communications Biology 2025) [paper] [code]
[Grow AI Virtual Cells] [Virtual Cell] [Review] Grow AI Virtual Cells: Three Data Pillars and Closed-Loop Learning (Cell Research 2025) [paper] [中文解读]
[Build the Virtual Cell] [Virtual Cell] [Review] Build the Virtual Cell with Artificial Intelligence: A Perspective for Cancer Research (Military Medical Research 2025) [paper]
[PS] [Perturbation] [Tool] Decoding Heterogeneous Single-Cell Perturbation Responses (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]
[Mixscale] [Perturbation] [Gene Regulation] [Multimodal] [Dataset] Systematic Reconstruction of Molecular Pathway Signatures Using Scalable Single-Cell Perturbation Screens (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]
[scDrugMap] [Benchmark] [Foundation Model] [Perturbation] [Tool] scDrugMap: benchmarking large foundation models for drug response prediction (Nature Communications 2025) [paper] [code]
[VCC Commentary] [Virtual Cell] [Benchmark] [Perturbation] [Review] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper] [homepage] [beginner's guidance]
[CZI Evaluation] [Benchmark] [Virtual Cell] [Review] [Evaluation & Measurement] Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop (arXiv 2025) [preprint] [中文解读]
[Virtual Organs] [Virtual Cell] [Benchmark] [Review] [Related] From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models (iCell 2025) [paper]
[GET] [Foundation Model] [Gene Regulation] [Multimodal] A Foundation Model of Transcription across Human Cell Types (Nature 2025) [paper] [code] [ask deepwiki]

🗓️ 2024 — 18 papers

[Zero-Shot Perturbation] [Foundation Model] [Perturbation] Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction (arXiv 2024) [preprint]
[TranSiGen] [Perturbation] [Representation Learning] [Intervention Design] Deep Representation Learning of Chemical-Induced Transcriptional Profile for Phenotype-Based Drug Discovery (Nature Communications 2024) [paper] [code] [ask deepwiki]
[PRnet] [Perturbation] [Intervention Design] Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery (Nature Communications 2024) [paper] [code]
[GenePT] [Foundation Model] [Representation Learning] Simple and Effective Embedding Model for Single-Cell Biology Built from ChatGPT (Nature Biomedical Engineering 2024) [paper] [code] [ask deepwiki]
[SCimilarity] [Foundation Model] [Representation Learning] [Tool] A Cell Atlas Foundation Model for Scalable Search of Similar Human Cells (Nature 2024) [paper] [code] [ask deepwiki]
[scFoundation] [Foundation Model] [Representation Learning] [Perturbation] [Gene Regulation] Large-Scale Foundation Model on Single-Cell Transcriptomics (Nature Methods 2024) [paper] [code] [ask deepwiki]
[scGPT] [Foundation Model] [Representation Learning] [Multimodal] [Perturbation] [Gene Regulation] scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI (Nature Methods 2024) [paper] [code] [ask deepwiki]
[TamGen] [Related] [Multimodal] TamGen: Drug Design with Target-Aware Molecule Generation through a Chemical Language Model (Nature Communications 2024) [paper] [code] [ask deepwiki] [publication code archive]
[GeneCompass] [Foundation Model] [Representation Learning] [Gene Regulation] [Perturbation] GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with a Knowledge-Informed Cross-Species Foundation Model (Cell Research 2024) [paper] [code] [ask deepwiki]
[scTab] [Foundation Model] [Representation Learning] scTab: Scaling Cross-Tissue Single-Cell Annotation Models (Nature Communications 2024) [paper] [code] [ask deepwiki]
[SATURN] [Foundation Model] [Representation Learning] Toward Universal Cell Embeddings: Integrating Single-Cell RNA-Seq Datasets across Species with SATURN (Nature Methods 2024) [paper] [code] [ask deepwiki]
[Cell2Sentence] [Foundation Model] [Multimodal] [Representation Learning] Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024) [paper] [code] [ask deepwiki]
[LangCell] [Foundation Model] [Multimodal] [Representation Learning] LangCell: Language-Cell Pre-training for Cell Identity Understanding (ICML 2024) [paper] [code] [ask deepwiki]
[CellPLM] [Foundation Model] [Representation Learning] [Spatial] CellPLM: Pre-training of Cell Language Model beyond Single Cells (ICLR 2024) [paper] [code] [ask deepwiki]
[scPROTEIN] [Protein] [Representation Learning] scPROTEIN: A Versatile Deep Graph Contrastive Learning Framework for Single-Cell Proteomics Embedding (Nature Methods 2024) [paper] [code]
[scLinear] [Protein] [Multimodal] scLinear Predicts Protein Abundance at Single-Cell Resolution (Communications Biology 2024) [paper] [code]
[Perturbation Proteomics] [Protein] [Perturbation] [Review] AI-Empowered Perturbation Proteomics for Complex Biological Systems (Cell Genomics 2024) [paper]
[Stanford PhD Thesis] [Virtual Cell] [Perturbation] [Intervention Design] [Review] Engineering Cells Using Artificial Intelligence (© by Yusuf Roohani 2024) [paper] [GitHub Homepage] [Arc profile]

📰 Latest Updates

Deadlines for the live competitions first, then a dated log of what changed in this list. Competition entries move into the log once they close.

Live challenges (dates as published by the organizers, checked on 21 August 2026)

Challenge	Phase now	Next milestone	Final call
Virtual Embryo Challenge (NeurIPS 2026)	P2 open: validation submissions scored and ranked, starter kit and reference baselines released 15 Aug 2026	Test phase opens 20 Oct 2026	Submissions close 2 Dec 2026, winners announced 11 Dec 2026
Virtual Cell Challenge 2026 (Arc Institute)	Validation phase open: leaderboard live since 20 Aug 2026	Final test set released 22 Oct 2026	Final submissions due 5 Nov 2026, 23:59 UTC; winners announced mid to late Nov 2026 (checked 1 Oct 2026)

Updates

2026-10-02 Expanded the structured catalog from 285 to 327 papers with 42 source-checked additions, covering late-September preprints, missed 2026 peer-reviewed methods, and foundation-model/perturbation evaluation work; refreshed the English and Chinese Start Here recommendations with seven question-led tracks and explicit preprint/manuscript labels; synchronized the catalog, landscape, CSV, and BibTeX exports.
2026-10-01 Added two perturbation atlases to Datasets: genome-scale Perturb-seq in primary human CD4+ T cells (Cell 2026) and the HepG2/Jurkat essential-gene screens with TRADE (Nature Genetics 2025); updated the Virtual Cell Challenge 2026 dates from the organizers' page.
2026-09-26 Added Evaluation & Measurement as a distinct branch; reclassified metric/measurement/benchmark-validity studies and added PertResolve, Signal–Bounds–Baselines, metric failure-mode analysis, and in-the-wild VCBench.
2026-09-26 Expanded Intervention Design with VCDesign, PDGrapher, PAIRING, PerturbNet, ARC, and NUDGE; CellNavi remains a core existing entry.
2026-09-26 Restored the full Research Papers list; reviewed all 275 records for multi-label topics and repository correspondence, with source-linked curation notes.
2026-09-25 September literature sweep: added Speciesformer, PHAROS, LucaCell, DeepSCENIC, scKITE, scRep, AnnFlux, new Cell world-model papers, the Virtual Cell Challenge 2026 paper, and the TIPS review; upgraded ProteinTalks to Nature 2026 and STATE, Tahoe-100M, and scBaseCount to their Cell 2026 publications.
2026-08-21 Added the Virtual Embryo Challenge (NeurIPS 2026), grouped both competitions under Challenges and Competitions, and started this News log.
2026-08-17 Added World Model and JEPA papers, refreshed preprint links, corrected stale venue labels.
2026-08-10 Added the Virtual Cell Challenge section covering both Arc editions.
2026-08-09 Added single-cell and protein papers, including scTranslator, CAPTAIN, and scPROTEIN.
2026-07-16 Added TCGA and HEST Xenium virtual spatial transcriptomics datasets (community PR).

Earlier updates

2026-07-14 Added DeepSpot2Cell (NeurIPS 2025) (community PR).
2026-07-06 Added 27 new 2026 papers from a May to July literature sweep.
2026-07-04 Added DeSCOPE (community PR).
2026-06-22 Added 2026 papers and missing code links.
2026-05-28 Expanded the Datasets section and added recent virtual cell papers.
2026-05-13 Added the automated literature update workflow, now manual dispatch only.
2026-03-18 Restructured the README, added the contributing guide, and linked the scientific figures section.

🧭 Browse the Repository

Start Here
Latest Updates
Research Papers
Overview Papers
Datasets
Challenges and Competitions
Reports and Blogs
Videos
Historical and Foundational Works
Related Resources

About, data, and maintenance

Scope
Inclusion Rules
Repository & Data
Contributing

📚 Overview Papers

Five high-signal overview and perspective papers are shown by default. Expand the rest only if you want broader background coverage.

[Nature Review] Revisiting the blueprint for an interpretable virtual cell (Nature Reviews Genetics 2026) [paper]
[Cell Review] A world model of the virtual cell (Cell 2026) [paper]
[Nature Perspective] Towards Multimodal Foundation Models in Molecular Cell Biology (Nature 2025) [paper] [中文解读]
[Nature Methods] The virtual cell (Nature Methods 2025) [paper]
[Cell Perspective] How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities (Cell 2024) [paper] [中文解读]

More overview and perspective papers (13)

[Nature News] Can AI Build a Virtual Cell? Scientists Race to Model Life's Smallest Unit (Nature 2025) [paper] [中文解读]
[Nature] The Human Cell Atlas from a cell census to a unified foundation model (Nature 2024) [paper]
[Nature Review] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]
[npj Digital Medicine] AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential (npj Digital Medicine 2025) [paper]
[Nature Review] Adapting systems biology to address the complexity of human disease in the single-cell era (Nature Reviews Genetics 2025) [paper]
[Nature Genetics] Causal machine learning for single-cell genomics (Nature Genetics 2025) [paper]
[Nature Methods] Multimodal foundation transformer models for multiscale genomics (Nature Methods 2025) [paper]
[Nature Methods] AI proteomics: from protein identification to virtual cells (Nature Methods 2026) [paper]
[Review] AI virtual cells for drug discovery and pharmacology (Trends in Pharmacological Sciences 2026) [paper]
[Cell Review] World models for biomedicine (Cell 2026) [paper]
[Nature Methods] Towards predictive virtual embryos with genomics and AI (Nature Methods 2026) [paper]
[Cell Perspective] Empowering Biomedical Discovery with AI Agents (Cell 2024) [paper] [中文解读]
[Cell Review] Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas (Cell 2024) [paper] [中文解读]

🗃️ Datasets

🧫 Perturbation and Cell-State Atlases

[Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [resource] [repo]
[Tahoe-100M] Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution (Cell 2026) [paper] [preprint] [code]
[X-Atlas/Orion] Genome-Wide Perturb-Seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models (bioRxiv 2025) [paper] [dataset]
[Primary CD4+ T cell Perturb-seq] Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits (Cell 2026) [paper] [dataset]
[TRADE: HepG2 and Jurkat Perturb-seq] Transcriptome-wide analysis of differential expression in perturbation atlases (Nature Genetics 2025) [paper] [dataset]

More resources (6)

[scPerturb] scPerturb: Harmonized Single-Cell Perturbation Data (Nature Methods 2024) [paper] [dataset] [code]
[CIGS] High-Throughput Profiling of Chemical-Induced Gene Expression across 93,644 Perturbations (Nature Methods 2025) [paper] [dataset] [code]
[L1000/CMap] A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles (Cell 2017) [paper] [dataset]
[Sci-Plex] Massively Multiplex Chemical Transcriptomics at Single-Cell Resolution (Science 2019) [paper] [dataset] [code]
[Perturb-seq datasets] Genome-scale and combinatorial Perturb-seq datasets from Replogle, Norman, Dixit, Adamson, and related screens [overview]
[Virtual Cell Challenge] Community perturbation-prediction challenge and hidden evaluation resources [homepage]

🔬 Single-Cell Reference Atlases

[scBaseCount] scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository (Cell 2026) [paper] [preprint] [code-scRecounter] [code-SRAgent]
[CZ CELLxGENE Discover] A single-cell data platform for scalable exploration, analysis, and modeling of aggregated data (NAR 2025) [paper] [dataset]
[Human Cell Atlas] International atlas of human cells and tissues [portal] [paper]

More resources (3)

[Tabula Sapiens] A multiple-organ single-cell transcriptomic atlas of humans (Science 2022) [paper] [dataset]
[Human BioMolecular Atlas Program] HuBMAP healthy human tissue atlas and common coordinate framework [portal] [paper]
[GTEx] Genotype-Tissue Expression project for human tissue expression baselines [portal] [overview]

🖼️ Multimodal, Morphology, and Imaging

[scGeneScope] scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling (NeurIPS 2025 Datasets and Benchmarks Track) [paper] [dataset]
[CPJUMP1] Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations (Nature Methods 2024) [paper] [dataset] [code]
[Cell Painting Gallery] Public high-content cell painting datasets from Broad and partners [dataset] [overview]

More resources (3)

[RxRx] Recursion high-content cellular imaging datasets for perturbation and batch-correction research [datasets]
[CM4AI] Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines (bioRxiv 2024) [paper] [dataset]
[STAMP] Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper]

🗺️ Spatial and Tissue Context

[HEST-1k] HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis (NeurIPS 2024) [paper] [code]
[TCGA virtual ST atlas] TCGA virtual spatial transcriptomics atlas: DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; ~28.7k slides / 32 cancer types) (medRxiv 2026) [paper] [dataset] [code]
[HEST Xenium virtual ST] HEST Xenium virtual spatial transcriptomics: DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (medRxiv 2026) [paper] [dataset] [code]

More resources (4)

[Spatial Perturb-seq] Spatial perturb-seq data for functional genomics within intact tissue architecture (Nature Communications 2026) [paper] [code]
[Perturb-FISH] CRISPR screening with imaging-based spatial transcriptomics (Cell 2025) [paper]
[10x Genomics spatial datasets] Visium, Xenium, and related public spatial transcriptomics example datasets [datasets]
[Vizgen MERFISH datasets] Public MERFISH example datasets for spatial transcriptomics [datasets]

🧬 Protein, Organelle, and Molecular Priors

[Human Protein Atlas] Subcellular and tissue protein expression atlases [resource] [subcellular] [paper]
[OpenCell] Endogenous protein tagging, localization, and interaction data for human cellular organization (Science 2022) [paper] [dataset]
[ProtiCelli] Proteome-wide image generation resources for human cell protein localization (bioRxiv 2026) [paper] [code]

More resources (3)

[SubCell] Proteome-aware microscopy foundation model resources based on HPA images (bioRxiv 2025) [paper] [code]
[STRING] Protein functional association and interaction networks [resource] [paper]
[OmniPath] Signaling, ligand-receptor, and causal network priors for multi-omics analysis [resource] [paper]

💊 Chemical, Drug, and Target Resources

[Drug Repurposing Hub] Curated compound library with targets, mechanisms, and clinical annotations (Nature Medicine 2017) [paper] [resource]
[DepMap/CCLE/PRISM] Cancer dependency, molecular profile, and drug-response resources for cell-line perturbation modeling [resource] [paper]
[ChEMBL] Drug-like molecules, bioactivities, targets, and assays [resource] [paper]

More resources (2)

[BindingDB] Protein-small molecule binding affinity knowledgebase [resource] [paper]
[PubChem] Compound identifiers, structures, assays, and bioactivity records [resource] [paper]

🏆 Challenges and Competitions

🧪 Virtual Cell Challenge (Arc Institute)

1st Edition (2025)

Question: can a computational model stand in for a real Perturb-seq experiment?
Task: predict the single-cell transcriptional response to held-out CRISPRi gene knockdowns, in a cell context deliberately chosen to sit off the training distribution of existing foundation models.
Data: a newly generated Perturb-seq dataset of about 300,000 H1 human embryonic stem cells (H1 hESC) covering 300 genetic perturbations, produced with 10x Genomics Flex chemistry and released in segments for training, validation, and testing.
Metrics: Perturbation Discrimination Score (PDS), Differential Expression Score (DES), and Mean Absolute Error (MAE), implemented in Arc's cell-eval suite. A Generalist Prize introduced during the competition re-ranked the 50 top-scoring final entries on the average rank across seven metrics: the three challenge metrics plus four from the STATE model paper (Pearson delta, Spearman correlation of log fold change, AUPRC, and Spearman correlation of effect size).
Timeline and scale: registration opened on 26 June 2025, winners were announced on 6 December 2025 at NeurIPS 2025. Over 5,000 people registered from 114 countries, over 1,200 teams submitted results, and over 300 teams made final submissions.
Winners:
- 1st place, $100k: BM_xTVC (BioMap Research), entry xTrimoSCPerturb, a hybrid of deep learning with classical statistical features, protein embeddings, and curated public data.
- 2nd place, $50k: XLearning Lab (Sichuan University, Tianfu Jincheng Lab), a metric-driven conditional generation framework over pseudo-bulk representations.
- 3rd place, $25k: Outlier (UChicago, Dartmouth, HKU), entry TransPert, cross-cell-line prediction from summary-level statistics with similarity-aware aggregation and global linear scaling.
- Generalist Prize, $100k: Altos Labs (Institute of Computation), entry go-with-the-flow, a flow matching generative model that learns time-dependent dynamics directly in gene expression space.
Reported takeaway: the winning entries all paired deep learning with classical statistical features. Arc's own wrap-up states that pure end-to-end neural networks did not yet outperform hybrid models. As of this entry none of the four winning entries has released code or a methods paper; the TransPert manuscript is described as forthcoming.
[Launch] Arc Institute launches its inaugural virtual cell competition (Arc Institute 2025) [announcement]
[Commentary] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper]
[Data] Behind the Data of the Virtual Cell Challenge (Arc Institute 2025) [post]
[Results] Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections (Arc Institute 2025) [post]
[Evaluation Code] cell-eval: the metrics suite used to score submissions [code]
[Metric Analysis] Effects of Distance Metrics and Scaling on the Perturbation Discrimination Score (arXiv 2025) [paper]

2nd Edition (2026)

Question: does a perturbation model transfer to cell contexts in which it has never seen a perturbation?
Task: multi-context generalization and zero-shot prediction. Participants predict how multiple cell lines respond to specified gene knockdowns, given only non-targeting control profiles for those lines. Predictions are scored against new experimental perturbation data that Arc generates from those cell lines and that no submitted model has trained on.
Data: no net-new training dataset this year. Arc states participants are free to use the H1 hESC Perturb-seq data released for the 2025 edition, plus any other public or private data they have access to.
Metrics: not public. Arc states that winners will be determined by a score reflecting evaluation criteria "to be released when the Challenge is launched".
Submission policy: entries do not require open weights or code. Arc frames this as a deliberate choice to encourage industry participation alongside academia and individual entrants. It also makes this edition less reproducible than the 2025 one, so treat leaderboard positions as claims rather than as verifiable results.
Timeline: submissions and the live leaderboard open on 20 August 2026, final results are announced in late November 2026.
Prizes: the top three models win prizes valued at $100,000, $50,000, and $25,000, each half cash and half NVIDIA Brev credits.
[Homepage] Virtual Cell Challenge (Arc Institute 2026) [homepage] [Virtual Cell Initiative]

🐣 Virtual Embryo Challenge (NeurIPS 2026)

Question: can a generative model predict how an embryo takes shape across space, scale, time, and perturbation?
Tasks: three, scored on shared hidden test sets. Temporal: predict gene-expression distributions at later developmental stages. Spatial-temporal: jointly predict expression and 3D location across scales, scored separately on the heart and on the whole embryo. Perturbation: predict knockout effects from wild-type data and observed perturbations.
Data: one MERFISH resource covering whole embryos and a heart-focused subset, about 1M cells across 11 time points from E6.75 to E12.5. The challenge page says "mammalian" without naming the species; the companion Virtual Embryo atlas is mouse, indexed by Theiler stage.
Tracks: two, scored separately on identical hidden test sets. The Human Team follows a conventional ML competition workflow. The Agent Team requires the run to belong to the coding agent or LLM-driven system: a human may write the initial prompt, but may not inspect intermediate results and feed judgement back in. Because both tracks are scored on the same held-out data, the agent track is a direct comparison against human-designed pipelines.
Metrics: not named on the challenge page as of 21 August 2026. The page states only that both tracks are scored on the same metrics and hidden test sets, so treat the scoring as unpublished for now.
Timeline: portal opened 10 August 2026, starter kit and reference baselines released 15 August 2026, test phase opens 20 October 2026, final submissions close 2 December 2026, winners announced 11 December 2026 at NeurIPS.
Prizes: $104K total from the Laude Institute Moonshots Seed Grant, split into $54K in winner prizes ($27K per track), $30K in travel awards, and a $20K Community Contribution Award.
[Homepage] The Virtual Embryo Challenge: Generative Modeling of Embryogenesis Across Space, Scale and Time (NeurIPS 2026 Competition Track) [homepage] [atlas] [NeurIPS announcement]
[Background] Towards Predictive Virtual Embryos with Genomics and AI (Nature Methods 2026) [paper]

📝 Reports and Blogs

[Post] Chan Zuckerberg Initiative's rBio Uses Virtual Cells to Train AI, Bypassing Lab Work (© by Michael Nuñez 2025) [blog]

More reports and blogs (5)

[Blog] What Are Virtual Cells? Learning "Universal Representations" of Life's Fundamental Unit (© by Elliot Hershberg 2025) [blog]

🎥 Videos

[Arc Institute] Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE [YouTube]
[Valence Labs] Virtual Cells: Predict, Explain, Discover [YouTube]
[EPFL] Virtual Cells and Digital Twins: AI in Personalized Medicine [YouTube]

More videos (4)

[SciLifeLab] Emma Lundberg: AI Virtual Cells Could Revolutionize Biological Science [YouTube]
[Chan Zuckerberg Initiative] AI Virtual Cell Models: How AI is Accelerating Science [YouTube]
[Chan Zuckerberg Initiative] CZI's Vision for AI-Powered "Virtual Cells" [YouTube]
[Podcast] Google DeepMind CEO: We Want to Build a Virtual Cell [YouTube]

🕰️ Historical and Foundational Works

[HPA Cell Atlas] [Morphology] A subcellular map of the human proteome (Science 2017) [paper] [resource]
[OpenCell] [Morphology] OpenCell: Endogenous tagging for the cartography of human cellular organization (Science 2022) [paper] [dataset]
[Perturb-seq] [Perturbation] Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (Cell 2022) [paper] [code]
[GEARS] [Perturbation] Predicting transcriptional outcomes of novel multigene perturbations with GEARS (Nature Biotechnology 2023) [paper] [code] [ask deepwiki]
[Geneformer] [Foundation Model] Transfer Learning Enables Predictions in Network Biology (Nature 2023) [paper] [code] [ask deepwiki]
[CellOT] [Perturbation] Learning Single-Cell Perturbation Responses Using Neural Optimal Transport (Nature Methods 2023) [paper] [code] [ask deepwiki]

More historical and foundational works (14)

[tGPT] [Foundation Model] Generative Pretraining from Large-Scale Transcriptomes for Single-Cell Deciphering (iScience 2023) [paper] [code] [ask deepwiki]
[Virtual Cell] Building the Next Generation of Virtual Cells to Understand Cellular Biology (Biophysical Journal 2023) [paper]
[Research Highlight] [Virtual Cell] Simulating a Whole Cell (Nature Methods 2022) [paper]
[sciPENN] [Protein] A Multi-Use Deep Learning Method for CITE-seq and Single-Cell RNA-seq Data Integration with Cell Surface Protein Prediction and Imputation (Nature Machine Intelligence 2022) [paper] [code]
[totalVI] [Protein] Joint Probabilistic Modeling of Single-Cell Multi-Omic Data with totalVI (Nature Methods 2021) [paper] [code]
[cTP-net] [Protein] Surface Protein Imputation from Single Cell Transcriptomes by Deep Neural Networks (Nature Communications 2020) [paper] [code]
[CITE-seq] [Protein] Simultaneous Epitope and Transcriptome Measurement in Single Cells (Nature Methods 2017) [paper]
[Comment] Personalized Medicine: Time for One-Person Trials (Nature Comment 2015) [paper]
[Theory] [Virtual Cell] A Whole-Cell Computational Model Predicts Phenotype from Genotype (Cell 2012) [paper]
[Virtual Cell] The Virtual Cell - A Candidate Co-Ordinator for "Middle-Out" Modelling of Biological Systems (BIB 2009) [paper]
[VCell 7.7] [Virtual Cell] Virtual Cell Modelling and Simulation Software Environment (IET Systems Biology 2008) [paper] [software]
Quantitative Cell Biology with the Virtual Cell (Trends in Cell Biology 2003) [paper]
[Review] The Virtual Cell: A Software Environment for Computational Cell Biology (Trends in Biotechnology 2001) [paper]
[Opinion] Whole-Cell Simulation: A Grand Challenge of the 21st Century (Trends in Biotechnology 2001) [paper]

[Virtual Cell Challenge] Official challenge site for evaluation and community updates [homepage]
[Virtual Embryo] Interactive mouse development atlas with 3D reconstructions, annotated sections, and open REST and MCP endpoints [site] [challenge]
[Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [repo]
[VCell Software] Long-running software environment for computational cell biology [site]
[Noetik OCTO-vc] Technical report and demo for virtual cells in tissue [report] [demo]

🎯 Scope

In scope: virtual cell perspectives, perturbation modeling, single-cell and multimodal foundation models, spatial and morphology modeling, biological AI agents, datasets, benchmarks, and community resources closely connected to virtual cell research.
Also included: adjacent work that is broadly useful for the virtual cell community, especially when it contributes data, evaluation methods, or modeling tools for cellular systems.
Usually out of scope: generic biomedical AI work with weak cell-modeling relevance, low-confidence secondary sources, broken links, or items that do not add clear value beyond more central references already listed here.

✅ Inclusion Rules

Prefer peer-reviewed papers, high-signal preprints, official project pages, and primary-source links.
Include code, datasets, project pages, or Chinese summaries when they are clearly useful.
Keep entries concise and broadly reusable for readers who are scanning the field.
Use the controlled multi-label taxonomy; assign tags from the actual task and evidence, not only model names.

🧰 Repository & Data

These links are mainly for reuse, contribution, and maintenance rather than day-to-day browsing.

Searchable catalog — filter papers by keyword, year, topic, and publication status.
Curation record — per-paper tagging rationale, source links and code-association checks.
Structured paper data — machine-readable JSON backing the Research Papers section.
Architecture — how structured data, generated views, validation, and automation fit together.
Automation — how literature updates are proposed and validated.

🤝 Contributing

If you want to suggest a paper, dataset, benchmark, blog, or project, open an Issue or Pull Request. Please follow CONTRIBUTING.md for the submission format and quality bar.

⭐ Star History

aivc

awesome

cell-biology

drug-discovery

foundation-models

multimodal-learning

single-cell

virtual-cell

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Boom5426/Awesome-Virtual-Cell

README

Awesome Virtual Cell

🚀 Start Here

🔬 Research Papers

🗓️ 2026 — 222 papers

🗓️ 2025 — 87 papers

🗓️ 2024 — 18 papers

📰 Latest Updates

🧭 Browse the Repository

📚 Overview Papers

🗃️ Datasets

🧫 Perturbation and Cell-State Atlases

🔬 Single-Cell Reference Atlases

🖼️ Multimodal, Morphology, and Imaging

🗺️ Spatial and Tissue Context

🧬 Protein, Organelle, and Molecular Priors

💊 Chemical, Drug, and Target Resources

🏆 Challenges and Competitions

🧪 Virtual Cell Challenge (Arc Institute)

1st Edition (2025)

2nd Edition (2026)

🐣 Virtual Embryo Challenge (NeurIPS 2026)

📝 Reports and Blogs

🎥 Videos

🕰️ Historical and Foundational Works

🔗 Related Resources

🎯 Scope

✅ Inclusion Rules

🧰 Repository & Data

🤝 Contributing

⭐ Star History

Similar repos

Languages

Boom5426/Awesome-Virtual-Cell

README

Awesome Virtual Cell

🚀 Start Here

🔬 Research Papers

🗓️ 2026 — 222 papers

🗓️ 2025 — 87 papers

🗓️ 2024 — 18 papers

📰 Latest Updates

🧭 Browse the Repository

📚 Overview Papers

🗃️ Datasets

🧫 Perturbation and Cell-State Atlases

🔬 Single-Cell Reference Atlases

🖼️ Multimodal, Morphology, and Imaging

🗺️ Spatial and Tissue Context

🧬 Protein, Organelle, and Molecular Priors

💊 Chemical, Drug, and Target Resources

🏆 Challenges and Competitions

🧪 Virtual Cell Challenge (Arc Institute)

1st Edition (2025)

2nd Edition (2026)

🐣 Virtual Embryo Challenge (NeurIPS 2026)

📝 Reports and Blogs

🎥 Videos

🕰️ Historical and Foundational Works

🔗 Related Resources

🎯 Scope

✅ Inclusion Rules

🧰 Repository & Data

🤝 Contributing

⭐ Star History

Similar repos

Languages