Awesome
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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Reviews
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The need to implement FAIR principles in biomolecular simulations [2024]
Amaro, Rommie, Johan Åqvist, Ivet Bahar, Federica Battistini, Adam Bellaiche, Daniel Beltran, Philip C. Biggin et al.
arXiv:2407.16584 (2024) -
An overview about neural networks potentials in molecular dynamics simulation [2024]
Martin‐Barrios, Raidel, Edisel Navas‐Conyedo, Xuyi Zhang, Yunwei Chen, and Jorge Gulín‐González.
International Journal of Quantum Chemistry 124.11 (2024) -
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
- mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics [2024]
Antonio Mirarchi, Toni Giorgino, G. D. Fabritiis.
arXiv:2407.14794 (2024) | code
Package
MMolearn
a Python package streamlining the design of generative models of biomolecular dynamics
https://github.com/LumosBio/MolData
Molecular dynamics
Molecular Force Fields
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HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information [2024]
Falbo, E. and Lavecchia, A.
J. Chem. Inf. Model. (2024) | code -
A Euclidean transformer for fast and stable machine learned force fields [2024]
Frank, J.T., Unke, O.T., Müller, KR. et al.
Nat Commun 15, 6539 (2024) | code -
Differentiable simulation to develop molecular dynamics force fields for disordered proteins [2024]
Greener, Joe G.
Chemical Science 15.13 (2024) | code -
Grappa--A Machine Learned Molecular Mechanics Force Field [2024]
Seute, Leif, Eric Hartmann, Jan Stühmer, and Frauke Gräter.
arXiv:2404.00050 (2024) | code -
An implementation of the Martini coarse-grained force field in OpenMM [2023]
MacCallum, J. L., Hu, S., Lenz, S., Souza, P. C., Corradi, V., & Tieleman, D. P.
Biophysical Journal 122.14 (2023)
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..
- StreaMD - A tool to perform high-throughput automated molecular dynamics simulations..
AI4MD Engines-Frameworks
- OpenMM 8 - Molecular Dynamics Simulation with Machine Learning Potentials.
- DeePMD-kit - A deep learning package for many-body potential energy representation and molecular dynamics.
- TorchMD - End-To-End Molecular Dynamics (MD) Engine using PyTorch.
- TorchMD-NET - TorchMD-NET provides state-of-the-art neural networks potentials (NNPs) and a mechanism to train them.
- 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.
- WEDAP - A Python Package for Streamlined Plotting of Molecular Simulation Data.
- Melodia - A Python library for protein structure analysis.
- MDANCE - A flexible n-ary clustering package that provides a set of tools for clustering Molecular Dynamics trajectories.
- PENSA - A collection of python methods for exploratory analysis and comparison of biomolecular conformational ensembles.
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
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Machine learning of force fields towards molecular dynamics simulations of proteins at DFT accuracy [2024]
Brunken, Christoph, Sebastien Boyer, Mustafa Omar, Bakary N'tji Diallo, Karim Beguir, Nicolas Lopez Carranza, and Oliver Bent.
ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design (2024) | code -
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) | code -
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 -
DeePMD-kit v2: A software package for deep potential models [2023]
Zeng, Jinzhe, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik et al.
The Journal of Chemical Physics 159.5 (2023) | code -
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics [2018]
Wang, Han, Linfeng Zhang, Jiequn Han, and E. Weinan.
Computer Physics Communications 228 (2018) | code
Neural Network Potentials
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Revisiting Aspirin Polymorphic Stability Using a Machine Learning Potential [2024]
Hattori, Shinnosuke, and Qiang Zhu.
ACS Omega (2024) -
Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials [2024]
Sabanés Zariquiey, F., Galvelis, R., Gallicchio, E., Chodera, J.D., Markland, T.E. and De Fabritiis, G.
J. Chem. Inf. Model. (2024) | code -
Universal-neural-network-potential molecular dynamics for lithium metal and garnet-type solid electrolyte interface [2024]
Iwasaki, R., Tanibata, N., Takeda, H. et al.
Commun Mater 5, 148 (2024) -
The Potential of Neural Network Potentials [2024]
Duignan, Timothy T.
ACS Physical Chemistry Au 4.3 (2024) -
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs [2024]
Anstine, Dylan, Roman Zubatyuk, and Olexandr Isayev.
chemrxiv-2023-296ch-v2 (2024) | code -
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics [2023]
Galvelis, R., Varela-Rial, A., Doerr, S., Fino, R., Eastman, P., Markland, T.E., Chodera, J.D. and De Fabritiis, G.
J. Chem. Inf. Model. (2023) | code -
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements [2022]
Takamoto, S., Shinagawa, C., Motoki, D. et al.
Nat Commun 13, 2991 (2022) | data -
Teaching a neural network to attach and detach electrons from molecules [2021]
Zubatyuk, R., Smith, J.S., Nebgen, B.T. et al.
Nat Commun 12, 4870 (2021) | code -
Four Generations of High-Dimensional Neural Network Potentials [2021]
Behler, Jorg.
Chemical Reviews 121.16 (2021) -
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models [2020]
Zhang, Yuzhi, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, and E. Weinan.
Computer Physics Communications 253 (2020) | code
Free Energy Perturbation
- Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening [2024]
Crivelli-Decker, J.E., Beckwith, Z., Tom, G., Le, L., Khuttan, S., Salomon-Ferrer, R., Beall, J., Gómez-Bombarelli, R. and Bortolato, A.
J. Chem. Theory Comput. (2024) | code
Deep Learning-molecular conformations
- GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling [2022]
Do, Hung N., Jinan Wang, Apurba Bhattarai, and Yinglong Miao.
J. Chem. Theory Comput. (2022) | code
AlphaFold-based
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Structure prediction of alternative protein conformations [2024]
Bryant, P., Noé, F.
Nat Commun 15, 7328 (2024) | code -
AlphaFold predictions of fold-switched conformations are driven by structure memorization [2024]
Chakravarty, D., Schafer, J.W., Chen, E.A. et al.
Nat Commun 15, 7296 (2024) | code -
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2 [2024]
Xingyue Guan and Qian-Yuan Tang and Weitong Ren and Mingchen Chen and Wei Wang and Peter G. Wolynes and Wenfei Li.
Proceedings of the National Academy of Sciences (2024) -
Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome [2024]
Francesco Trozzi, Oanh Tran, Carmen Al Masri, Shu-Hang Lin, Balaguru Ravikumar, Rayees Rahman.
bioRxiv (2024) -
A resource for comparing AF-Cluster and other AlphaFold2 sampling methods [2024]
Hannah K Wayment-Steele, Sergey Ovchinnikov, Lucy Colwell, Dorothee Kern.
bioRxiv (2024) -
Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK [2024]
Ohnuki, Jun, and Kei-ichi Okazaki.
The Journal of Physical Chemistry B (2024) -
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2 [2024]
Yogesh Kalakoti, Björn Wallner.
bioRxiv (2024) | code -
Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling [2024]
Nishank Raisinghani, Mohammed Alshahrani, Grace Gupta, Hao Tian, Sian Xiao, Peng Tao, Gennady Verkhivker.
bioRxiv (2024) | code -
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 -
AlphaFold2-RAVE: From Sequence to Boltzmann Ranking [2023]
Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, and Pratyush Tiwary.
J. Chem. Theory Comput. (2023)) | code -
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures [2023]
Carmen Al-Masri, Francesco Trozzi, Shu-Hang Lin, Oanh Tran, Navriti Sahni, Marcel Patek, Anna Cichonska, Balaguru Ravikumar, Rayees Rahman.
Bioinformatics Advances. (2023)) | 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
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AbFlex: Predicting the conformational flexibility of antibody CDRs [2024]
Spoendlin, Fabian C., Wing Ki Wong, Guy Georges, Alexander Bujotzek, and Charlotte Deane.
ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications (2024) | code -
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
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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.
J. Chem. Theory Comput. (2024) | 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
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Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning [2024]
Zhu, Yu, Jing Gu, Zhuoran Zhao, AW Edith Chan, Maria F. Mojica, Andrea M. Hujer, Robert A. Bonomo, and Shozeb Haider.
J. Chem. Inf. Model. (2024) | data -
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
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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
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Frame-to-Frame Coarse-grained Molecular Dynamics with SE (3) Guided Flow Matching [2024]
Li, Shaoning, Yusong Wang, Mingyu Li, Jian Zhang, Bin Shao, Nanning Zheng, and Jian Tang
arXiv:2405.00751 (2024) -
AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.
arXiv:2402.04845 (2024) | code
Diffusion-based
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4D Diffusion for Dynamic Protein Structure Prediction with Reference Guided Motion Alignment [2024]
Cheng, Kaihui, Ce Liu, Qingkun Su, Jun Wang, Liwei Zhang, Yining Tang, Yao Yao, Siyu Zhu, and Yuan Qi.
arXiv:2408.12419 (2024) -
Generating Multi-state Conformations of P-type ATPases with a Diffusion Model [2024]
Jingtian Xu, Yong Wang.
bioRxiv (2024) | code -
Transferable deep generative modeling of intrinsically disordered protein conformations [2024]
Abdin, O., Kim, P.M.
PLOS Computational Biology 20.5 (2024) | code -
Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion [2024]
Janson, Giacomo, and Michael Feig.
Nat Mach Intell 6, 775–786 (2024) | code -
Accurate Conformation Sampling via Protein Structural Diffusion [2024]
Fan, Jiahao, Ziyao Li, Eric Alcaide, Guolin Ke, Huaqing Huang, and Weinan E.
bioRxiv (2024) -
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion [2023]
Grambow, Colin A., Hayley Weir, Nathaniel Diamant, Alex Tseng, Tommaso Biancalani, Gabriele Scalia and Kangway V Chuang.
arXiv:2305.19800 (2023) | code
Score-based
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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
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Enabling Population Protein Dynamics Through Bayesian Modeling [2024]
Sylvain Lehmann, Jérôme Vialaret, Audrey Gabelle, Luc Bauchet, Jean-Philippe Villemin, Christophe Hirtz, Jacques Colinge.
Bioinformatics (2024) -
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
Molecular conformational ensembles by methods
Small molecule conformational ensembles
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Diffusion-based generative AI for exploring transition states from 2D molecular graphs [2024]
Kim, S., Woo, J. & Kim, W.Y.
Nat Commun 15, 341 (2024) | code -
Physics-informed generative model for drug-like molecule conformers [2024]
David C. Williams, Neil Imana.
arXiv:2403.07925. (2024) | code -
COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework [2024]
Kuznetsov, Maksim, Fedor Ryabov, Roman Schutski, Rim Shayakhmetov, Yen-Chu Lin, Alex Aliper, and Daniil Polykovskiy.
J. Chem. Inf. Model. (2024) | code -
Leveraging 2D Molecular Graph Pretraining for Improved 3D Conformer Generation with Graph Neural Networks [2024]
Jiang, Runxuan, Tarun Gogineni, Joshua Kammeraad, Yifei He, Ambuj Tewari, and Paul M. Zimmerman.
Computers & Chemical Engineering (2024) | code -
DynamicsDiffusion: Generating and Rare Event Sampling of Molecular Dynamic Trajectories Using Diffusion Models [2023]
Petersen, Magnus, Gemma Roig, and Roberto Covino.
NeurIPS 2023 AI4Science (2023) -
Generating Molecular Conformer Fields [2023]
Yuyang Wang, Ahmed Elhag, Navdeep Jaitly, Joshua Susskind, Miguel Bautista.
[NeurIPS 2023 Generative AI and Biology (GenBio) Workshop (2023)]https://openreview.net/forum?id=Od1KtMeAYo) -
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space [2023]
Zhou, Z., Liu, R. and Yu, T.
arXiv:2310.04915 (2023)) -
Molecular Conformation Generation via Shifting Scores [2023]
Zhou, Zihan, Ruiying Liu, Chaolong Ying, Ruimao Zhang, and Tianshu Yu.
arXiv:2309.09985 (2023) -
EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency [2023]
Fan, Zhiguang, Yuedong Yang, Mingyuan Xu, and Hongming Chen.
arXiv:2308.00237 (2023) -
Prediction of Molecular Conformation Using Deep Generative Neural Networks [2023]
Xu, Congsheng, Yi Lu, Xiaomei Deng, and Peiyuan Yu.
Chinese Journal of Chemistry(2023) -
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks [2023]
Zhu, Yanqiao, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du et al.
NeurIPS 2023 AI for Science Workshop. 2023 (2023) | code -
Deep-Learning-Assisted Enhanced Sampling for Exploring Molecular Conformational Changes [2023]
Haohao Fu, Han Liu, Jingya Xing, Tong Zhao, Xueguang Shao, and Wensheng Cai.
J. Phys. Chem. B (2023) -
Torsional diffusion for molecular conformer generation [2022]
Jing, Bowen, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.
NeurIPS. (2022) | code -
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation [2022]
Xu, Minkai, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang.
International Conference on Learning Representations. (2022) | code -
Conformer-RL: A deep reinforcement learning library for conformer generation [2022]
Jiang, Runxuan, Tarun Gogineni, Joshua Kammeraad, Yifei He, Ambuj Tewari, and Paul M. Zimmerman.
Journal of Computational Chemistry 43.27 (2022) | code -
Energy-inspired molecular conformation optimization [2022]
Guan, Jiaqi, Wesley Wei Qian, Wei-Ying Ma, Jianzhu Ma, and Jian Peng.
International Conference on Learning Representations. (2022) | code -
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming [2021]
Xu, Minkai, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, and Jian Tang.
International Conference on Machine Learning. PMLR (2021) | code
RNA conformational ensembles
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On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules [2024]
Muscat, Stefano, Gianfranco Martino, Jacopo Manigrasso, Marco Marcia, and Marco De Vivo.
J. Chem. Theory Comput. (2024) -
Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations [2018]
Bottaro, S., Bussi, G., Kennedy, S.D., Turner, D.H. and Lindorff-Larsen, K.
Science advances 4.5 (2018) | code | data
Peptide conformational ensembles
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CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning [2024]
Grambow, C.A., Weir, H., Cunningham, C.N. et al.
Sci Data 11, 859 (2024) | code -
Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion [2024]
Abdin, O., Kim, P.M.
Nat Mach Intell 6, 775–786 (2024) | code -
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion [2023]
Grambow, Colin A., Hayley Weir, Nathaniel Diamant, Alex Tseng, Tommaso Biancalani, Gabriele Scalia and Kangway V Chuang.
arXiv:2305.19800 (2023) | code
Protein conformational ensembles
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Structure prediction of alternative protein conformations [2024]
Bryant, P., Noé, F.
Nat Commun 15, 7328 (2024) | code -
Deep learning guided design of dynamic proteins [2024]
Amy B. Guo, Deniz Akpinaroglu, Mark J.S. Kelly, Tanja Kortemme.
bioRxiv. (2024) -
AlphaFold predictions of fold-switched conformations are driven by structure memorization [2024]
Chakravarty, D., Schafer, J.W., Chen, E.A. et al.
Nat Commun 15, 7296 (2024) | code -
4D Diffusion for Dynamic Protein Structure Prediction with Reference Guided Motion Alignment [2024]
Cheng, Kaihui, Ce Liu, Qingkun Su, Jun Wang, Liwei Zhang, Yining Tang, Yao Yao, Siyu Zhu, and Yuan Qi.
arXiv:2408.12419 (2024) -
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2 [2024]
Xingyue Guan and Qian-Yuan Tang and Weitong Ren and Mingchen Chen and Wei Wang and Peter G. Wolynes and Wenfei Li.
Proceedings of the National Academy of Sciences (2024) -
A resource for comparing AF-Cluster and other AlphaFold2 sampling methods [2024]
Hannah K Wayment-Steele, Sergey Ovchinnikov, Lucy Colwell, Dorothee Kern.
bioRxiv (2024) -
Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK [2024]
Ohnuki, Jun, and Kei-ichi Okazaki.
The Journal of Physical Chemistry B (2024) -
Transferable deep generative modeling of intrinsically disordered protein conformations [2024]
Abdin, O., Kim, P.M.
PLOS Computational Biology 20.5 (2024) | code -
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2 [2024]
Yogesh Kalakoti, Björn Wallner.
bioRxiv (2024) | code -
Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling [2024]
Nishank Raisinghani, Mohammed Alshahrani, Grace Gupta, Hao Tian, Sian Xiao, Peng Tao, Gennady Verkhivker.
bioRxiv (2024) | code -
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 -
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 -
Frame-to-Frame Coarse-grained Molecular Dynamics with SE (3) Guided Flow Matching [2024]
Li, Shaoning, Yusong Wang, Mingyu Li, Jian Zhang, Bin Shao, Nanning Zheng, and Jian Tang
arXiv:2405.00751 (2024) -
AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.
arXiv:2402.04845 (2024) | code -
Machine learning of force fields towards molecular dynamics simulations of proteins at DFT accuracy [2024]
Brunken, Christoph, Sebastien Boyer, Mustafa Omar, Bakary N'tji Diallo, Karim Beguir, Nicolas Lopez Carranza, and Oliver Bent.
ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design (2024) | code -
Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning [2024]
Zhu, Yu, Jing Gu, Zhuoran Zhao, AW Edith Chan, Maria F. Mojica, Andrea M. Hujer, Robert A. Bonomo, and Shozeb Haider.
J. Chem. Inf. Model. (2024) | data -
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 -
Str2str: A score-based framework for zero-shot protein conformation sampling [2024]
Lu, Jiarui, Bozitao Zhong, Zuobai Zhang, and Jian Tang.
ICLR (2024) | code -
Enabling Population Protein Dynamics Through Bayesian Modeling [2024]
Sylvain Lehmann, Jérôme Vialaret, Audrey Gabelle, Luc Bauchet, Jean-Philippe Villemin, Christophe Hirtz, Jacques Colinge.
Bioinformatics (2024) -
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 -
Score-based enhanced sampling for protein molecular dynamics [2023]
Lu, Jiarui, Bozitao Zhong, and Jian Tang.
arXiv:2306.03117 (2023) | code -
AlphaFold2-RAVE: From Sequence to Boltzmann Ranking [2023]
Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, and Pratyush Tiwary.
J. Chem. Theory Comput. (2023)) | code -
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures [2023]
Carmen Al-Masri, Francesco Trozzi, Shu-Hang Lin, Oanh Tran, Navriti Sahni, Marcel Patek, Anna Cichonska, Balaguru Ravikumar, Rayees Rahman.
Bioinformatics Advances. (2023)) | 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 -
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 -
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 -
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 -
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 -
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 -
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 -
Energy-based models for atomic-resolution protein conformations [2020]
Du, Yilun, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives.
ICLR (2020) | code
Enzymes conformational ensembles
-
Generating Multi-state Conformations of P-type ATPases with a Diffusion Model [2024]
Jingtian Xu, Yong Wang.
bioRxiv (2024) | code -
Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning [2024]
Zhu, Yu, Jing Gu, Zhuoran Zhao, AW Edith Chan, Maria F. Mojica, Andrea M. Hujer, Robert A. Bonomo, and Shozeb Haider.
J. Chem. Inf. Model. (2024) | data
Antibody conformational ensembles
- AbFlex: Predicting the conformational flexibility of antibody CDRs [2024]
Spoendlin, Fabian C., Wing Ki Wong, Guy Georges, Alexander Bujotzek, and Charlotte Deane.
ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications (2024) | code
Ligand-Protein conformational ensembles
-
MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery [2024]
Siebenmorgen, T., Menezes, F., Benassou, S. et al.
Nat Comput Sci 4, 367–378 (2024) | code -
Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials [2024]
Sabanés Zariquiey, F., Galvelis, R., Gallicchio, E., Chodera, J.D., Markland, T.E. and De Fabritiis, G.
J. Chem. Inf. Model. (2024) | code -
Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction [2024]
Poziemski, Jakub, Artur Yurkevych, and Pawel Siedlecki.
chemrxiv-2024-dxv36 (2024) | code -
Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding [2022]
Wu, Fang, Shuting Jin, Yinghui Jiang, Xurui Jin, Bowen Tang, Zhangming Niu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, and Stan Z. Li.
Advanced Science 9.33 (2022) | code
PPI conformational ensembles
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Quantifying conformational changes in the TCR:pMHC-I binding interface [2024]
Benjamin McMaster, Christopher Thorpe, Jamie Rossjohn, Charlotte M. Deane, Hashem Koohy.
bioRxiv (2024) | code -
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.
J. Chem. Theory Comput. (2024) | bioRxiv (2024) | code -
Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence [2023]
Su, Zhaoqian, Kalyani Dhusia, and Yinghao Wu.
bioRxiv (2023) -
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) | code
RNA-Peptide conformational ensembles
- Enhanced Sampling Simulations of RNA-peptide Binding using Deep Learning Collective Variables [2024]
Nisha Kumari, Sonam Dhull, Tarak Karmakar.
bioRxiv (2024)
Material ensembles
-
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange [2024]
Gomez, A., Thompson, W.H. & Laage, D.
Nat. Chem. (2024) | data -
Universal-neural-network-potential molecular dynamics for lithium metal and garnet-type solid electrolyte interface [2024]
Iwasaki, R., Tanibata, N., Takeda, H. et al.
Commun Mater 5, 148 (2024)