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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning
| Date | Stars |
|---|---|
| 2026-07-24 | 304 |
| 2026-07-25 | 305 |
| 2026-07-28 | 305 |
| 2026-07-30 | 305 |
| 2026-07-31 | 306 |
| 2026-08-06 | 306 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
1.0
growth rate 0.33%/day
[](https://github.com/AspirinCode/awesome-AI4MolConformation-MD)
## awesome-AI4MolConformation-MD
List of **molecules ( small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations** and **molecular dynamics (force fields)** using **generative artificial intelligence** and **deep learning**
**Updating ...**
## Menu
- [Deep Learning-molecular conformations](#deep-learning-molecular-conformations)
| Menu | Menu | Menu | Menu |
| ------ | :---------- | ------ | ------ |
| [Reviews](#reviews) | [Datasets and Package](#datasets-and-package) | [Molecular dynamics](#molecular-dynamics) | [Molecular Force Fields](#molecular-force-fields) |
| [Neural Molecular Force Fields](#neural-molecular-force-fields) | [MD Engines-Frameworks](#md-engines-frameworks) | [AI4MD Engines-Frameworks](#ai4md-engines-frameworks) | [MD Trajectory Processing-Analysis](#md-trajectory-processing-analysis) |
| [Visualization](#visualization) | [CGMD](#cgmd) | [AI4MD](#ai4md) | [Neural Network Potentials](#neural-network-potentials) |
| [Neural Reactive Potential](#neural-reactive-potential) | [Reactive Force Fields](#reactive-force-fields) | [Free Energy Perturbation](#free-energy-perturbation) | [Solvent Potential](#solvent-potential) |
| [Theoretical Chemistry](#theoretical-chemistry) | [QuantumChem](#quantumchem) | [Ab Initio](#ab-initio) | [AI-QuantumChem](#ai-quantumchem) |
| [AlphaFold-based](#alphaFold-based) | [Autoregressive-Based](#autoregressive-Based) | [LSTM-based](#lstm-based) | [Transformer-based](#transformer-based) |
| [VAE-based](#vae-based) | [GAN-based](#gan-based) | [Flow-based](#flow-based) | [Flow Matching-based](#flow-matching-based) |
| [Diffusion-based](#diffusion-based) | [Score-Based](#score-Based) | [Energy-based](#energy-based) | [Bayesian-based](#bayesian-based) |
| [Active Learning-based](#active-learning-based) | [GNN-based](#gnn-based) | [LLM-MD](#llm-md) | [Agent-based-MD](#agent-based-md) |
- [Molecular conformational dynamics by methods](#molecular-conformational-dynamics-by-methods)
| Menu | Menu | Menu |
| ------ | :---------- | ------ |
| [Small molecule conformational dynamics](#small-molecule-conformational-dynamics) | [RNA conformational dynamics](#rna-conformational-dynamics) | [Peptide conformational dynamics](#peptide-conformational-dynamics) |
| [Protein conformational dynamics](#protein-conformational-dynamics) | [Enzymes conformational dynamics](#enzymes-conformational-dynamics) | [Antibody conformational dynamics](#antibody-conformational-dynamics) |
| [Ligand-Protein conformational dynamics](#ligand-protein-conformational-dynamics) | [RNA-Peptide conformational dynamics](#rna-peptide-conformational-dynamics) | [PPI conformational dynamics](#ppi-conformational-dynamics) |
| [Antibody-Protein conformational dynamics](#antibody-protein-conformational-dynamics) | [Nucleic acid-Protein conformational dynamics](#nucleic-acid-protein-conformational-dynamics) | [Material ensembles](#material-dynamics) |
| [Nucleic acid-Ligand conformational dynamics](#nucleic-acid-ligand-conformational-dynamics) | | |
## Reviews
* **Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches** [2026]
Ruqaiya Khalil, Elena Frasnetti, Han Kurt, Tareq Hameduh, Mohd Athar, Giorgio Colombo, and Attilio Vittorio Vargiu.
[J. Phys. Chem. Lett. (2026)](https://doi.org/10.1021/acs.jpclett.6c01412)
* **Replacing Quantum Chemistry With Machine-Learned Interatomic Potentials: Revolution or Evolution?** [2026]
Andrew J. Medford and David S. Sholl.
[ACS Cent. Sci.(2026)](https://doi.org/10.1021/acscentsci.6c00615)
* **Toward a unified framework for determining conformational ensembles of disordered proteins** [2Excerpt of 246,600 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:53b900d3ec0df5ea, topic:llm, topic:transformer
matched fp:53b900d3ec0df5ea, topic:gan