Awesome Machine Learning in Small Molecules Mass Spectrometry

Mass spectrometry, also called mass spec, is an analytical technique that is used to measure the mass-to-charge ratio of ions. The results are presented as a mass spectrum, a plot of intensity as a function of the mass-to-charge ratio.

from Wikipedia

Keep updating the awesome machine-learning papers and codes related to small molecules mass spectrometry. Please notice that awesome lists are curations of the best, not everything. Contributes are always welcome!

Contents

• Databases
  • Molecular properties
  • Mass spectrometry
  • Retention time
  • Collision cross section
• Papers
  • Survey/Review papers
  • Discussions in databases
  • Small molecular representation learning
    • Point-based (or quantum-based) methods
    • Graph-based methods
    • Sequence-based methods
  • Mass spectrometry-related properties prediction
    • Tandem mass spectra prediction
    • Retention time prediction
    • Collision cross section prediction
  • Mass spectra representation learning and matching
  • Mass spectra peak annotation/assignment
  • Chemical formula prediction from mass spectra
  • De novo molecular structure elucidation from MS/MS spectra
• Machine learning in small molecules chromatography
• Related awesome lists

Databases

Molecular properties (background):

MS/MS:

EAD MS/MS:

Retention time:

Collision cross section:

Papers

Survey/Review papers

• [Annu. Rev. Anal. Chem. 2025] Hong, Yuhui, et al. Machine Learning in Small-Molecule Mass Spectrometry
• [J. Am. Soc. Mass Spectrom. 2024] Nguyen, Julia, et al. Advancing the Prediction of MS/MS Spectra Using Machine Learning
• [IJCAI 2023] Xia, Jun, et al. A Systematic Survey of Chemical Pre-trained Models
• [Metabolomics 2022] Bittremieux, Wout, Mingxun Wang, and Pieter C. Dorrestein. The critical role that spectral libraries play in capturing the metabolomics community knowledge
• [TrAC 2021] Debus, Bruno, et al. Deep learning in analytical chemistry
• [J. Cheminform. 2013] Scheubert, Kerstin, et al. Computational mass spectrometry for small molecules
• [JCIM 2023] Zhang, Ziqiao, et al. Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?
• [NeurIPS 2022] Sun, Ruoxi, et al. Does GNN Pretraining Help Molecular Representation?

Discussions in databases

• [Nat. Commun 2025] Kretschmer, Fleming, et al. Coverage bias in small molecule machine learning
• [Anal. Chem. 2024] Hoang, Corey, et al. Tandem Mass Spectrometry across Platforms
• [bioRxiv 2024] Kretschmer, Fleming, et al. Small molecule machine learning: All models are wrong, some may not even be useful

Small molecular representation learning

According to the information embedded in the model, the molecular representation learning models are categorized as point-based (or quantum-based) methods, graph-based methods, and sequence-based methods. Because the number of graph-based methods is huge, they are further divided into self-supervised learning and supervised learning manners. It is worth noting that the difference between point-based (or quantum-based) methods and graph-based methods is if bonds (i.e. edges) are included in the encoding.

Point-based (or quantum-based) methods

• [ICLR 2023] Zhou, Gengmo, et al. Uni-mol: A universal 3d molecular representation learning framework [code]
• [PMLR 2021] Schütt, Kristof, et al. Equivariant message passing for the prediction of tensorial properties and molecular spectra [code]
• [NeurIPS 2017] Schütt, Kristof, et al. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions [code]

Graph-based methods

Self-Supervised Learning:

• [Brief. Bioinformatics 2024] Zhen, Wang, et al. BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation [code]
• [Bioinformatics 2023] [3DGCL] Moon, Kisung, et al. 3D graph contrastive learning for molecular property prediction [code]
• [ICLR 2023] [Mole-BERT] Xia, Jun, et al. Mole-bert: Rethinking pre-training graph neural networks for molecules [code]
• [ICLR 2023 (spotlight)] [GNS TAT] Zaidi, Sheheryar, et al. Pre-training via denoising for molecular property prediction [code]
• [ICLR 2023] [GeoSSL-DDM] Liu, Shengchao, et al. Molecular geometry pretraining with se (3)-invariant denoising distance matching [code]
• [ICLR 2022] [GraphMVP] Liu, Shengchao, et al. Pre-training molecular graph representation with 3d geometry [code]
• [NeurIPS 2021] [MGSSL] Zhang, Zaixi, et al. Motif-based graph self-supervised learning for molecular property prediction [code]
• [NeurIPS 2020] [GROVER] Rong, Yu, et al. Self-supervised graph transformer on large-scale molecular data [code]
• [ICLR 2020] [InfoGraph] Sun, Fan-Yun, et al. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization [code]

Supervised Learning

• [AAAI 2023] [Molformer] Wu, Fang, et al. Molformer: Motif-based transformer on 3d heterogeneous molecular graphs [code]
• [NeurIPS 2022] [ComENet] Wang, Limei, et al. ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs [code (implemented in DIG library)]
• [ICLR 2022] [GNS+Noisy Nodes] Godwin, Jonathan, et al. Simple GNN regularisation for 3D molecular property prediction & beyond [codes]
• [ICLR 2022] [MolR] Wang, Hongwei, et al. Chemical-reaction-aware molecule representation learning [code]
• [ICLR 2022] [SphereNet] Liu, Yi, et al. Spherical message passing for 3d graph networks [code (implemented in DIG library)]
• [Nat. Mach. Intell. 2022] [GEM] Fang, Xiaomin, et al. Geometry-enhanced molecular representation learning for property prediction [code]
• [NeurIPS 2021] [GemNet] Gasteiger, Johannes, et al. Gemnet: Universal directional graph neural networks for molecules [code]
• [NeurIPS 2020] [DimeNet++] Klicpera, Johannes, et al. Fast and uncertainty-aware directional message passing for non-equilibrium molecules [code]
• [ICLR 2020] [DimeNet] Gasteiger, Johannes, et al. Directional message passing for molecular graphs [code]
• [Chem. Mater 2019] [MEGNet] Chen, Chi, et al. Graph networks as a universal machine learning framework for molecules and crystals [preprint] [code]
• [PMLR 2017] Gilmer, Justin, et al. Neural message passing for quantum chemistry [code]
• [NeurIPS 2015] [Neural FPs] Duvenaud, David K., et al. Convolutional networks on graphs for learning molecular fingerprints [code]

Other Related Works

• [NeurIPS 2020] You, Yuning, et al. Graph contrastive learning with augmentations [code]
• [ICLR 2020] Hu, Weihua, et al. Strategies for pre-training graph neural networks [code]

Sequence-based methods

• [Patterns 2022] [SELFIES] Krenn, Mario, et al. SELFIES and the future of molecular string representations [code]
• [Nat. Mach. Intell. 2022] [MolFormer] Ross, Jerret, et al. Large-scale chemical language representations capture molecular structure and properties [code]
• [Chem. Sci. 2022] [R-SMILES] Zhong, Zipeng, et al. Root-aligned SMILES: a tight representation for chemical reaction prediction [code]
• [BCB 2019] [SMILES-BERT] Wang, Sheng, et al. SMILES-BERT: large scale unsupervised pre-training for molecular property prediction [code]

Mass spectrometry-related properties prediction

Tandem mass spectra prediction

• [Nat. Commun 2025] [FIORA] Nowatzky, Yannek, et al. FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events [code]
• [Anal. Chem. 2024] [PPGB_MS2] Zheng, Fujian, et al. Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph [code]
• [Nat. Mach. Intell. 2024] Young, Adamo, et al. Tandem mass spectrum prediction for small molecules using graph transformers [code]
• [Anal. Chem. 2023] Wang, Fei, et al. Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances [code]
• [Nat. Mach. Intell. 2023] Goldman, Samuel, et al. Annotating metabolite mass spectra with domain-inspired chemical formula transformers [code]
• [NeurIPS 2023] Goldman, Samuel, et al. Prefix-tree decoding for predicting mass spectra from molecules [code]
• [Bioinformatics 2023] Hong, Yuhui, et al. 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations [code]
• [Anal. Chem. 2021] Wang, Fei, et al. CFM-ID 4.0: more accurate ESI-MS/MS spectral prediction and compound identification [code]
• [ACS Cent. Sci. 2019] Wei, Jennifer N., et al. Rapid prediction of electron–ionization mass spectrometry using neural networks [code]

Retention time prediction

• [Bioinformatics 2024] [RT-Transformer] Xue, Jun, et al. RT-Transformer: Retention time prediction for metabolite annotation to assist in metabolite identification [code]
• [J. Chromatogr. A 2023] [DeepGCN-RT] Kang, Qiyue, et al. Deep graph convolutional network for small-molecule retention time prediction [code]
• [Anal. Chem. 2021] [GNN-RT] Yang, Qiong, et al. Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification [code]
• [Anal. Chem. 2020] [Retip] Bonini, Paolo, et al. Retip: retention time prediction for compound annotation in untargeted metabolomics [code]
• [Nat. Commun 2019] Domingo-Almenara, Xavier, et al. The METLIN small molecule dataset for machine learning-based retention time prediction [code]

Collision cross section prediction

• [Anal. Chem. 2024] de Cripan, et al. Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules
• [Anal. Chem. 2022] [AllCCS2] Zhang, Haosong, et al. AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations [code]
• [Anal. Chem. 2022] [CCSP 2.0] Rainey, Markace A., et al. CCS Predictor 2.0: An open-source jupyter notebook tool for filtering out false positives in metabolomics [code]
• [Nat. Commun 2020] [AllCCS] Zhou, Zhiwei, et al. Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics [code]
• [Anal. Chem. 2019] [DeepCCS] Plante, Pier-Luc, et al. Predicting ion mobility collision cross-sections using a deep neural network: DeepCCS [code]

Mass spectra representation learning and matching

• [Nat. Biotechnol. 2025] [DreaMS] Bushuiev, Roman, et al. Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS [code]
• [Anal. Chem. 2024] [MSBERT] Zhang, Hailing, et al. MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning [code]
• [Anal. Chem. 2023] [CLERMS] Guo, Hao, et al. Contrastive learning-based embedder for the representation of tandem mass spectra [code]
• [Nat. Commun 2023] [FastEI] Yang, Qiong, et al. Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library [code]
• [PLoS Comput. Biol. 2021] Huber, Florian, et al. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships [code]
• [J. Cheminform. 2021] Huber, Florian, et al. MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra [code]
• [Anal. Chem. 2019] [DeepMASS] Ji, Hongchao, et al. Deep MS/MS-aided structural-similarity scoring for unknown metabolite identification [code]

Mass spectra peak annotation/assignment

• [J. Cheminform. 2016] Ruttkies, Christoph, et al. MetFrag relaunched: incorporating strategies beyond in silico fragmentation [website]
• [Anal. Chem. 2014] Ma, Yan, et al. MS2Analyzer: A software for small molecule substructure annotations from accurate tandem mass spectra [website]
• [Nucleic Acids Res. 2014] Allen, Felicity, et al. CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra [website] CFM-ID is designed for three tasks: spectrum prediction, peak assignment, and compound identification.

Chemical formula prediction from mass spectra

• [Nat. Commun 2025] [FIDDLE] Hong, Yuhui, et al. FIDDLE: a deep learning method for chemical formulas prediction from tandem mass spectra [code]
• [Nat. Commun 2025] [MetDNA3] Zhang, Haosong, et al. Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics [code]
• [JCIM 2023] Goldman, Samuel, et al. MIST-CF: Chemical formula inference from tandem mass spectra [code]
• [Nat. Methods 2023] [BUDDY] Xing, Shipei, et al. BUDDY: molecular formula discovery via bottom-up MS/MS interrogation [code1] [code2]
• [Nat. Methods 2019] [SIRIUS 4] Dührkop, Kai, et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information [code]

De novo molecular structure elucidation from MS/MS spectra

• [ICLR 2026] [FlowMS] Nie, Jianan, et al. FlowMS: Flow Matching for De Novo Structure Elucidation from Mass Spectra
• [NeurIPS 2025] [MS-BART] Han, Yang, et al. MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation [code]
• [ICML 2025] [DiffMS] Bohde, Marshall, et al. DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [code]
• [Nat. Methods 2022] [MSNovelist] Stravs, Michael A., et al. MSNovelist: de novo structure generation from mass spectra [code]

Machine learning in small molecules chromatography

Mass spectrometry is often coupled with chromatographic techniques, such as GC-MS (gas chromatography-mass spectrometry) or LC-MS (liquid chromatography-mass spectrometry). In these combined techniques, the chromatographic method separates the compounds, and then the mass spectrometer analyzes each separated compound for identification and quantification.

• [Anal. Chem. 2024] [3DMolCSP] Hong, Yuhui, et al. Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations [code]
• [Nat. Commun 2023] [QGeoGNN] Xu, Hao, et al. Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network [code]
• [J. Sep. Sci. 2018] Piras, Patrick, et al. Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy
• [J. Chromatogr. A 2016] Sheridan, Robert, et al. Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers

Related awesome lists

• Awesome Mass Spectra Libraries: This repository contains the latest libraries for mass spectral data and related properties.
• Awesome Small Molecule Machine Learning: This repository focuses on machine learning topics related to small molecules.
• Awesome Cheminformatics: This repository concentrates on computer-based methods in chemistry.
• Awesome Python Chemistry: This repository is dedicated to Python-based frameworks, libraries, software, and resources in the field of Chemistry.
• Awesome DeepBio & deeplearning-biology: These repositories focus on deep learning methods in biology.
• awesome-pretrain-on-molecules