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

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• Deep Learning-molecular conformations

Menu	Menu	Menu	Menu
Reviews	Datasets and Package	Molecular dynamics	Molecular Force Fields
Neural Molecular Force Fields	MD Engines-Frameworks	AI4MD Engines-Frameworks	MD Trajectory Processing-Analysis
Visualization	CGMD	AI4MD	Neural Network Potentials
Neural Reactive Potential	Reactive Force Fields	Free Energy Perturbation	Solvent Potential
Theoretical Chemistry	QuantumChem	Ab Initio	AI-QuantumChem
AlphaFold-based	Autoregressive-Based	LSTM-based	Transformer-based
VAE-based	GAN-based	Flow-based	Flow Matching-based
Diffusion-based	Score-Based	Energy-based	Bayesian-based
Active Learning-based	GNN-based	LLM-MD	Agent-based-MD

• Molecular conformational dynamics by methods

Menu	Menu	Menu
Small molecule conformational dynamics	RNA conformational dynamics	Peptide conformational dynamics
Protein conformational dynamics	Enzymes conformational dynamics	Antibody conformational dynamics
Ligand-Protein conformational dynamics	RNA-Peptide conformational dynamics	PPI conformational dynamics
Antibody-Protein conformational dynamics	Nucleic acid-Protein conformational dynamics	Material ensembles
Nucleic acid-Ligand conformational dynamics

Reviews

• Replacing Quantum Chemistry With Machine-Learned Interatomic Potentials: Revolution or Evolution? [2026]
  Andrew J. Medford and David S. Sholl.
  ACS Cent. Sci.(2026)

• Toward a unified framework for determining conformational ensembles of disordered proteins [2026]
  Ghafouri, H., Kadeřávek, P., Melo, A.M. et al.
  Nat Methods (2026)

• Graph neural networks for molecular dynamics simulations [2026]
  Ahsan, Mohd, Chinmai Pindi, Souvik Sinha, Amun C. Patel, and Giulia Palermo.
  Current Opinion in Structural Biology (2026)

• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications [2025]
  Kai Zhu, Enrico Trizio, Jintu Zhang, Renling Hu, Linlong Jiang, Tingjun Hou, Luigi Bonati.
  arXiv:2509.04291 (2025)

• Generative AI techniques for conformational diversity and evolutionary adaptation of proteins [2025]
  Brownless, Alfie-Louise R., Dariia Yehorova, Colin L. Welsh, and Shina Caroline Lynn Kamerlin.
  Curr Opin Struct Biol. (2025)

• Generation of protein dynamics by machine learning [2025]
  Janson G, Feig M..
  Curr Opin Struct Biol. (2025)

• A critical review of machine learning interatomic potentials and Hamiltonian [2025]
  Li, Y.; Zhang, X.; Liu, M.; Shen, L.
  J. Mater. Inf. (2025)

• Generation of protein dynamics by machine learning [2025]
  Janson, Giacomo, and Michael Feig.
  Current Opinion in Structural Biology 93 (2025)

• Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era [2025]
  Xinyue Cui, Lingyu Ge, Xia Chen, Zexin Lv, Suhui Wang, Xiaogen Zhou, Guijun Zhang.

Datasets and Package

Datasets

• DynaRepo: The repository of macromolecular conformational dynamics [2025]
  Omid Mokhtari, Emmanuelle Bignon, Hamed Khakzad, Yasaman Karami.
  Nucleic Acids Research (2025) | bioRxiv (2025) | data

• MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials [2025]
  Tristan Maxson, Ademola Soyemi, Xinglong Zhang, Benjamin W. J. Chen, and Tibor Szilvási.
  J. Chem. Inf. Model. (2025) | data

• A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials [2025]
  Fu, Cong, Yuchao Lin, Zachary Krueger, Wendi Yu, Xiaoning Qian, Byung-Jun Yoon, Raymundo Arróyave et al.
  arXiv:2506.23008 (2025) | data

• The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models [2025]
  Levine, Daniel S., Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G. Taylor, Muhammad R. Hasyim, Kyle Michel, Ilyes Batatia, G'abor Cs'anyi, Misko Dzamba, Peter K. Eastman, Nathan C. Frey, Xiang Fu, Vahe Gharakhanyan, Aditi S. Krishnapriyan, Joshua A. Rackers, Sanjeev Raja, Ammar Rizvi, Andrew S. Rosen, Zachary W. Ulissi, Santiago Vargas, C. Lawrence Zitnick, Samuel M. Blau and Brandon M. Wood.
  arXiv:2505.08762 (2025) | data

• Enhanced Sampling, Public Dataset and Generative Model for Drug-Protein Dissociation Dynamics [2025]
  Maodong Li, Jiying Zhang, Bin Feng, Wenqi Zeng, Dechin Chen, Zhijun Pan, Yu Li, Zijing Liu, Yi Isaac Yang.
  arXiv:2504.18367 (2025) | data

• QMe14S: A Comprehensive and Efficient Spectral Data Set for Small Organic Molecules [2025]
  Cristian Gabellini, Nikhil Shenoy, Stephan Thaler, Semih Canturk, Daniel McNeela, Dominique Beaini, Michael Bronstein, Prudencio Tossou. |