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 ...

• Deep Learning-molecular conformations

• Molecular conformational ensembles by methods

• 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)

• mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics [2024]
  Antonio Mirarchi, Toni Giorgino, G. D. Fabritiis.
  arXiv:2407.14794 (2024) | code

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

https://github.com/LumosBio/MolData

• 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)

• 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..

• 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.

• 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.

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

• 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.

• 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

• Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning [2024]
  Sharma, A., Sanvito, S.
  npj Comput Mater 10, 237 (2024) | code

• Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields [2024]
  Kabylda A, Frank JT, Dou SS, Khabibrakhmanov A, Sandonas LM, Unke OT, et al.
  ChemRxiv. (2024) | code

• AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics [2024]
  Mirarchi, Antonio, Raul P. Pelaez, Guillem Simeon, and Gianni De Fabritiis.
  arXiv:2409.17852 (2024) | code

• 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

• Automated Adaptive Absolute Binding Free Energy Calculations [2024]
  Clark, Finlay, Graeme Robb, Daniel Cole, and Julien Michel.
  J. Chem. Theory Comput. (2024) | code

• 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

• Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields [2024]
  Kabylda A, Frank JT, Dou SS, Khabibrakhmanov A, Sandonas LM, Unke OT, et al.
  ChemRxiv. (2024) | code

• SpaiNN: Equivariant Message Passing for Excited-State Nonadiabatic Molecular Dynamics [2024]
  Mausenberger, Sascha, Carolin Müller, Alexandre Tkatchenko, Philipp Marquetand, Leticia González, and Julia Westermayr.
  Chemical Science (2024) | code

• 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

• AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants [2024]
  Núñez-Franco, Reyes, M. Milagros Muriel-Olaya, Gonzalo Jiménez-Osés, and Francesca Peccati.
  J. Chem. Inf. Model. (2024) | data

• AlphaFold Ensemble Competition Screens Enable Peptide Binder Design with Single-Residue Sensitivity [2024]
  Vosbein, Pernille, Paula Paredes Vergara, Danny T. Huang, and Andrew R. Thomson.
  ACS Chemical Biology (2024)

• Assessing AF2’s ability to predict structural ensembles of proteins [2024]
  Riccabona, Jakob R., Fabian C. Spoendlin, Anna-Lena M. Fischer, Johannes R. Loeffler, Patrick K. Quoika, Timothy P. Jenkins, James A. Ferguson et al.
  Structure (2024)

• AlphaFold with conformational sampling reveals the structural landscape of homorepeats [2024]
  Bonet, David Fernandez et al.
  Structure (2024) | code

• 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

• 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

• 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

• 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

• Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy [2024]
  Sanaa Mansoor, Minkyung Baek, Hahnbeom Park, Gyu Rie Lee, and David Baker.
  Advanced Science (2024)

• 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

• 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

• Generative Modeling of Molecular Dynamics Trajectories [2024]
  Jing, Bowen, Hannes Stark, Tommi Jaakkola, and Bonnie Berger.
  ICML'24 Workshop ML for Life and Material Science: From Theory to Industry Applications (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

• 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.
  J. Chem. Inf. Model. (2024) | bioRxiv (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

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

• 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

• SeaMoon: Prediction of molecular motions based on language models [2024]
  Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine.
  bioRxiv. (2024) | code

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

• 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

• 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

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• Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion [2023]
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• Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy [2024]
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• AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics [2024]
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• Conformations of KRAS4B Affected by Its Partner Binding and G12C Mutation: Insights from GaMD Trajectory-Image Transformation-Based Deep Learning [2024]
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• Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies [2024]
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• 4D Diffusion for Dynamic Protein Structure Prediction with Reference Guided Motion Alignment [2024]
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• Predicting protein conformational motions using energetic frustration analysis and AlphaFold2 [2024]
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• A resource for comparing AF-Cluster and other AlphaFold2 sampling methods [2024]
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• Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK [2024]
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• Transferable deep generative modeling of intrinsically disordered protein conformations [2024]
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• AFsample2: Predicting multiple conformations and ensembles with AlphaFold2 [2024]
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• 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]
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• Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE [2024]
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  arXiv:2404.07102 (2024)

• High-throughput prediction of protein conformational distributions with subsampled AlphaFold2 [2024]
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• AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
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• Predicting multiple conformations via sequence clustering and AlphaFold2 [2024]
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  Nature 625, 832–839 (2024) | code

• Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers [2024]
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• Frame-to-Frame Coarse-grained Molecular Dynamics with SE (3) Guided Flow Matching [2024]
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• AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
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• Machine learning of force fields towards molecular dynamics simulations of proteins at DFT accuracy [2024]
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• Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning [2024]
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• Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling [2024]
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• Enabling Population Protein Dynamics Through Bayesian Modeling [2024]
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• Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space [2023]
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• AlphaFold2-RAVE: From Sequence to Boltzmann Ranking [2023]
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• Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures [2023]
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• Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures [2023]
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• Sampling alternative conformational states of transporters and receptors with AlphaFold2 [2022]
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• LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories [2022]
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• Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [2022]
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• ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Conformational Space [2021]
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• Explore protein conformational space with variational autoencoder [2021]
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• Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning [2024]
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• AbFlex: Predicting the conformational flexibility of antibody CDRs [2024]
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• Modeling protein-small molecule conformational ensembles with ChemNet [2024]
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  bioRxiv (2024)

• MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery [2024]
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• Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials [2024]
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• Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction [2024]
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• Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding [2022]
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• Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD [2024]
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• Quantifying conformational changes in the TCR:pMHC-I binding interface [2024]
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• Exploring the conformational ensembles of protein-protein complex with transformer-based generative model [2024]
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• Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence [2023]
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  bioRxiv (2023)

• Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity [2024]
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• Enhanced Sampling Simulations of RNA-peptide Binding using Deep Learning Collective Variables [2024]
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  bioRxiv (2024)

• Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning [2024]
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• Generative AI model trained by molecular dynamics for rapid mechanical design of architected graphene [2024]
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  Extreme Mechanics Letters (2024)

• Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange [2024]
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• Universal-neural-network-potential molecular dynamics for lithium metal and garnet-type solid electrolyte interface [2024]
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