shunsunsun/awesome-AI4ProteinConformation-MD

List of protein conformations and molecular dynamics using generative artificial intelligence and deep learning

awesome-AI4ProteinConformation-MD

List of protein (and PPIs) conformations and molecular dynamics (MD) using generative artificial intelligence and deep learning

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• Deep Learning-protein conformations

Menu	Menu	Menu	Menu
Reviews	Datasets and Package	Molecular dynamics	AI4MD
AlphaFold-based	GNN-based	LSTM-based	Transformer-based
VAE-based	GAN-based	Flow-based
Score-Based	Energy-based	Bayesian-based	Active Learning-based
LLM-MD

Reviews

• Artificial Intelligence Enhanced Molecular Simulations [2023]
  Zhang, Jun, Dechin Chen, Yijie Xia, Yu-Peng Huang, Xiaohan Lin, Xu Han, Ningxi Ni et al.
  J. Chem. Theory Comput. (2023)

• Machine Learning Generation of Dynamic Protein Conformational Ensembles [2023]
  Zheng, Li-E., Shrishti Barethiya, Erik Nordquist, and Jianhan Chen.
  Molecules 28.10 (2023)

Datasets and Package

Datasets

Package

MMolearn
a Python package streamlining the design of generative models of biomolecular dynamics

https://github.com/LumosBio/MolData

Molecular dynamics

MD Engines/Frameworks

• Amber - A suite of biomolecular simulation programs.
• Gromacs - A molecular dynamics package mainly designed for simulations of proteins, lipids and nucleic acids.
• OpenMM - A toolkit for molecular simulation using high performance GPU code.
• CHARMM - A molecular simulation program with broad application to many-particle systems.
• HTMD - Programming Environment for Molecular Discovery.
• ACEMD - The next generation molecular dynamic simulation software.
• NAMD - A parallel molecular dynamics code for large biomolecular systems..

AI4MD Engines/Frameworks

• TorchMD - End-To-End Molecular Dynamics (MD) Engine using PyTorch.
• OpenMM-Torch - OpenMM plugin to define forces with neural networks.

MD Trajectory Processing/Analysis

• MDAnalysis - An object-oriented Python library to analyze trajectories from molecular dynamics (MD) simulations in many popular formats.
• MDTraj - A python library that allows users to manipulate molecular dynamics (MD) trajectories.
• PyTraj - A Python front-end package of the popular cpptraj program.
• CppTraj - Biomolecular simulation trajectory/data analysis.

Reference

https://github.com/ipudu/awesome-molecular-dynamics

Visualization

• VMD - A molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting.
• NGLview - IPython widget to interactively view molecular structures and trajectories.
• PyMOL - A user-sponsored molecular visualization system on an open-source foundation, maintained and distributed by Schrödinger.
• Avogadro - An advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.

AI4MD

• Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity [2024]
  Jeffrey K Weber, Joseph A Morrone, Seung-gu Kang, Leili Zhang, Lijun Lang, Diego Chowell, Chirag Krishna, Tien Huynh, Prerana Parthasarathy, Binquan Luan, Tyler J Alban, Wendy D Cornell, Timothy A Chan.
  Briefings in Bioinformatics (2024) | data

• Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments [2024]
  Unke, Oliver T., Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin et al.
  Science Advances 10.14 (2024) | data

Deep Learning-protein conformations

AlphaFold-based

• Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE [2024]
  Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary.
  arXiv:2404.07102 (2024)

• High-throughput prediction of protein conformational distributions with subsampled AlphaFold2 [2024]
  Monteiro da Silva, G., Cui, J.Y., Dalgarno, D.C. et al.
  Nat Commun 15, 2464 (2024) | code

• AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
  Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.
  arXiv:2402.04845 (2024) | code

• Predicting multiple conformations via sequence clustering and AlphaFold2 [2024]
  Wayment-Steele, H.K., Ojoawo, A., Otten, R. et al.
  Nature 625, 832–839 (2024) | code

• Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures [2023]
  Herrington, Noah B., David Stein, Yan Chak Li, Gaurav Pandey, and Avner Schlessinger.
  bioRxiv (2023) | code

• Sampling alternative conformational states of transporters and receptors with AlphaFold2 [2022]
  Del Alamo, Diego, Davide Sala, Hassane S. Mchaourab, and Jens Meiler.
  Elife 11 (2022) | code

GNN-based

• RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints [2024]
  Huang, Ying, Huiling Zhang, Zhenli Lin, Yanjie Wei, and Wenhui Xi.
  bioRxiv (2024) | code

LSTM-based

• Learning molecular dynamics with simple language model built upon long short-term memory neural network [2020]
  Tsai, ST., Kuo, EJ. & Tiwary, P.
  Nat Commun 11, 5115 (2020) | code

Transformer-based

• Exploring the conformational ensembles of protein-protein complex with transformer-based generative model [2024]
  Wang, Jianmin, Xun Wang, Yanyi Chu, Chunyan Li, Xue Li, Xiangyu Meng, Yitian Fang, Kyoung Tai No, Jiashun Mao, and Xiangxiang Zeng.
  bioRxiv (2024) | code

• Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers [2024]
  Chennakesavalu, Shriram, and Grant M. Rotskoff.
  The Journal of Physical Chemistry B (2024) | code

• Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [2022]
  Schwing, Gregory, Luigi L. Palese, Ariel Fernández, Loren Schwiebert, and Domenico L. Gatti.
  arXiv:2206.04683 (2022) | code

VAE-based

• Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling [2024]
  Sanaa Mansoor, Minkyung Baek, Hahnbeom Park, Gyu Rie Lee, and David Baker.
  J. Chem. Theory Comput. (2024)

• Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling [2024]
  Junjie Zhu, Zhengxin Li, Haowei Tong, Zhouyu Lu, Ningjie Zhang, Ting Wei and Hai-Feng Chen.
  Briefings in Bioinformatics. (2024) | code

• Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Auotencoder [2023]
  JunJie Zhu, NingJie Zhang, Ting Wei and Hai-Feng Chen.
  International Journal of Molecular Sciences. (2023) | code

• Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence [2023]
  Su, Zhaoqian, Kalyani Dhusia, and Yinghao Wu.
  bioRxiv (2023)

• Artificial intelligence guided conformational mining of intrinsically disordered proteins [2022]
  Gupta, A., Dey, S., Hicks, A. et al.
  Commun Biol 5, 610 (2022) | code

• LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories [2022]
  Tian, Hao, Xi Jiang, Sian Xiao, Hunter La Force, Eric C. Larson, and Peng Tao
  J. Chem. Inf. Model. (2022) | code

• Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [2022]
  Schwing, Gregory, Luigi L. Palese, Ariel Fernández, Loren Schwiebert, and Domenico L. Gatti.
  arXiv:2206.04683 (2022) | code

• ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Conformational Space [2021]
  Tatro, Norman Joseph, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, and Rongjie Lai.
  ICLR (2022)

• Explore protein conformational space with variational autoencoder [2021]
  Tian, Hao, Xi Jiang, Francesco Trozzi, Sian Xiao, Eric C. Larson, and Peng Tao.
  Frontiers in molecular biosciences 8 (2021) | code

GAN-based

• Direct generation of protein conformational ensembles via machine learning [2023]
  Janson, G., Valdes-Garcia, G., Heo, L. et al.
  Nat Commun 14, 774 (2023) | code

• Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [2022]
  Schwing, Gregory, Luigi L. Palese, Ariel Fernández, Loren Schwiebert, and Domenico L. Gatti.
  arXiv:2206.04683 (2022) | code

Flow-based

• AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
  Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.
  arXiv:2402.04845 (2024) | code

Score-based

• Str2str: A score-based framework for zero-shot protein conformation sampling [2024]
  Lu, Jiarui, Bozitao Zhong, Zuobai Zhang, and Jian Tang.
  ICLR (2024) | code

• Score-based enhanced sampling for protein molecular dynamics [2023]
  Lu, Jiarui, Bozitao Zhong, and Jian Tang.
  arXiv:2306.03117 (2023) | code

Energy-based

• Energy-based models for atomic-resolution protein conformations [2020]
  Du, Yilun, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives.
  ICLR (2020) | code

Bayesian-based

• Deep Boosted Molecular Dynamics (DBMD): Accelerating molecular simulations with Gaussian boost potentials generated using probabilistic Bayesian deep neural network [2023]
  Do, Hung N., and Yinglong Miao.
  bioRxiv(2023) | code

• Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space [2023]
  Draizen, Eli J., Stella Veretnik, Cameron Mura, and Philip E. Bourne.
  bioRxiv(2023) | code

Active Learning-based

• Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets [2023]
  Kleiman, Diego E., and Diwakar Shukla.
  J. Chem. Theory Comput. (2023) | code

LLM-MD

• Molecular simulation with an LLM-agent [2024]
  MD-Agent is a LLM-agent based toolset for Molecular Dynamics.
  code