EXXOGEN is a DeepTech biotechnology startup redefining molecular recognition through a novel, first-principles (ab initio) analytical framework for quantum-level interactions.
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README
EXXOGEN
EXXOGEN is a DeepTech biotechnology startup redefining molecular recognition through a novel, first-principles (ab initio) analytical framework for quantum-level interactions.
Core Breakthrough: Bypassing iterative Density Functional Theory (DFT) approximations and supercomputer overhead, EXXOGEN utilizes a novel, proprietary analytical operator derived from fundamental quantum principles. Rather than repackaging existing open-source engines, the proprietary operator computes the quantum-level dynamics in the background, outputting the structured and refined energy matrix as seen in the benchmark datasets, delivering near instantaneous computation and massive scalability.
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EXXOGEN Quantum Benchmark Sample v1.0 (10 Molecules)
This benchmark dataset provides quantum-level transport boundaries, interaction classifications, and binding energy matrices computed via the EXXOGEN engine's proprietary first-principles framework.
As a high-fidelity, noise-filtered physical dataset, it serves as a ground-truth foundation for computational chemistry and molecular machine learning. By training or conditioning AI architectures on physically consistent quantum state matrices, researchers can significantly eliminate structural hallucinations and accelerate the rational design of viable, physically accurate molecules.
Contents:
8 Reference Molecules: Ground-truth validation set with experimental coordinates.
2 AlphaFold Predicted Structures: Demonstrating quantum energy annotation on unindexed structural models.
Benchmark Instructions:
Run these 10 samples through your Graph Neural Networks (GNNs), AlphaFold fine-tuning pipelines, or molecular dynamics models to evaluate physical consistency.
Feedback & Full Dataset Requests:
Found a bug or have performance feedback? Let us know at kisnorbert87@gmail.com
Need the industrial 200,000 molecule dataset? Contact us for early access.
License
This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to use, share, adapt, and train models on this dataset for both non-commercial and commercial purposes, provided appropriate attribution is given to EXXOGEN.
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EXXOGEN is a DeepTech biotechnology startup redefining molecular recognition through a novel, first-principles (ab initio) analytical framework for quantum-level interactions.
1
stars
27
commits
Sep 3, 2026
updated
README
EXXOGEN
EXXOGEN is a DeepTech biotechnology startup redefining molecular recognition through a novel, first-principles (ab initio) analytical framework for quantum-level interactions.
Core Breakthrough: Bypassing iterative Density Functional Theory (DFT) approximations and supercomputer overhead, EXXOGEN utilizes a novel, proprietary analytical operator derived from fundamental quantum principles. Rather than repackaging existing open-source engines, the proprietary operator computes the quantum-level dynamics in the background, outputting the structured and refined energy matrix as seen in the benchmark datasets, delivering near instantaneous computation and massive scalability.
.........
EXXOGEN Quantum Benchmark Sample v1.0 (10 Molecules)
This benchmark dataset provides quantum-level transport boundaries, interaction classifications, and binding energy matrices computed via the EXXOGEN engine's proprietary first-principles framework.
As a high-fidelity, noise-filtered physical dataset, it serves as a ground-truth foundation for computational chemistry and molecular machine learning. By training or conditioning AI architectures on physically consistent quantum state matrices, researchers can significantly eliminate structural hallucinations and accelerate the rational design of viable, physically accurate molecules.
Contents:
8 Reference Molecules: Ground-truth validation set with experimental coordinates.
2 AlphaFold Predicted Structures: Demonstrating quantum energy annotation on unindexed structural models.
Benchmark Instructions:
Run these 10 samples through your Graph Neural Networks (GNNs), AlphaFold fine-tuning pipelines, or molecular dynamics models to evaluate physical consistency.
Feedback & Full Dataset Requests:
Found a bug or have performance feedback? Let us know at kisnorbert87@gmail.com
Need the industrial 200,000 molecule dataset? Contact us for early access.
License
This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to use, share, adapt, and train models on this dataset for both non-commercial and commercial purposes, provided appropriate attribution is given to EXXOGEN.
See what people are saying
Similar repos
Contributors
27 commits