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https://github.com/aspirincode/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
https://github.com/aspirincode/awesome-ai4molconformation-md
List: awesome-ai4molconformation-md
active-learning amber enzymes flow force-fields gan gnns gromacs llm lstm-neural-networks molecular-dynamics neural-network-potentials openmm ppi proteinconformations transformer vae
Last synced: 21 days ago
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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
Host: GitHub
URL: https://github.com/aspirincode/awesome-ai4molconformation-md
Owner: AspirinCode
License: gpl-3.0
Created: 2024-04-10T05:19:13.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-10-30T05:42:07.000Z (about 2 months ago)
Last Synced: 2024-10-30T08:37:33.571Z (about 2 months ago)
Topics: active-learning, amber, enzymes, flow, force-fields, gan, gnns, gromacs, llm, lstm-neural-networks, molecular-dynamics, neural-network-potentials, openmm, ppi, proteinconformations, transformer, vae
Homepage:
Size: 4.6 MB
Stars: 117
Watchers: 4
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-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. (Other Lists / Monkey C Lists)
README

        [![License: GPL](https://img.shields.io/badge/License-GPL-yellow)](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**

![Protein Space and Conformations](https://github.com/AspirinCode/awesome-AI4MolConformation-MD/blob/main/figure/afpro.png)

**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) |

| [MD Engines-Frameworks](#md-engines-frameworks) | [AI4MD Engines-Frameworks](#ai4md-engines-frameworks) | [MD Trajectory Processing-Analysis](#md-trajectory-processing-analysis) | [AI4MD](#ai4md) |

| [Neural Network Potentials](#neural-network-potentials) | [Free Energy Perturbation](#free-energy-perturbation) | [Ab Initio](#ab-initio) |  |

| [AlphaFold-based](#alphaFold-based) | [GNN-based](#gnn-based)  | [LSTM-based](#lstm-based) | [Transformer-based](#transformer-based) |

| [VAE-based](#vae-based) | [GAN-based](#gan-based) | [Flow-based](#flow-based) | [Diffusion-based](#diffusion-based) |

| [Score-Based](#score-Based) | [Energy-based](#energy-based) | [Bayesian-based](#bayesian-based) | [Active Learning-based](#active-learning-based) |

| [LLM-MD](#llm-md) |  |  |  |

  - [Molecular conformational ensembles by methods](#molecular-conformational-ensembles-by-methods)

| Menu | Menu | Menu |

| ------ | :---------- | ------ |

| [Small molecule conformational ensembles](#small-molecule-conformational-ensembles) | [RNA conformational ensembles](#rna-conformational-ensembles) | [Peptide conformational ensembles](#peptide-conformational-ensembles) |

| [Protein conformational ensembles](#protein-conformational-ensembles) | [Enzymes conformational ensembles](#enzymes-conformational-ensembles)  | [Antibody conformational ensembles](#antibody-conformational-ensembles) |

| [Ligand-Protein conformational ensembles](#ligand-protein-conformational-ensembles) | [RNA-Peptide conformational ensembles](#rna-peptide-conformational-ensembles) | [PPI conformational ensembles](#ppi-conformational-ensembles) |

| [Antibody-Protein conformational ensembles](#antibody-protein-conformational-ensembles) |  | [Material ensembles](#material-ensembles) |

## Reviews

* **Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles** [2024]   

 Erdős, G., & Dosztányi, Z.   

  [Current opinion in structural biology (2024)](https://doi.org/10.1016/j.sbi.2024.102950)  

* **Recent advances in protein conformation sampling by combining machine learning with molecular simulation** [2024]   

 Tang, Y., Yang, Z., Yao, Y., Zhou, Y., Tan, Y., Wang, Z., Pan, T., Xiong, R., Sun, J. and Wei, G.   

  [Chinese Physics B. (2024)](https://iopscience.iop.org/article/10.1088/1674-1056/ad1a92)  

* **Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery** [2024]   

 Qian, Runtong, Jing Xue, You Xu, and Jing Huang.   

  [J. Chem. Inf. Model. (2024)](https://doi.org/10.1021/acs.jcim.4c01024)  

* **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)](https://arxiv.org/abs/2407.16584)  

* **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)](https://doi.org/10.1002/qua.27389)  

* **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)](https://doi.org/10.1021/acs.jctc.3c00214)  

* **Machine Learning Generation of Dynamic Protein Conformational Ensembles** [2023]   

 Zheng, Li-E., Shrishti Barethiya, Erik Nordquist, and Jianhan Chen.   

  [Molecules 28.10 (2023)](https://doi.org/10.3390/molecules28104047)  

## Datasets and Package

### Datasets

* **Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials** [2024]  

Antonio Mirarchi, Toni Giorgino, G. D. Fabritiis.   

[ChemRxiv. (2024)](https://doi.org/10.26434/chemrxiv-2024-w3ld0-v2) | [code](https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs)  

* **mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics** [2024]  

Antonio Mirarchi, Toni Giorgino, G. D. Fabritiis.   

[	arXiv:2407.14794 (2024)](https://arxiv.org/abs/2407.14794) | [code](https://github.com/compsciencelab/mdCATH)  

### Package

**MMolearn**  

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

https://github.com/LumosBio/MolData   

## Molecular dynamics

### Molecular Force Fields

* **HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information** [2024]  

Falbo, E. and Lavecchia, A.   

[J. Chem. Inf. Model. (2024)](https://doi.org/10.1021/acs.jcim.4c00540) | [code](https://github.com/emanuelefalbo/HessFit)  

* **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)](https://doi.org/10.1038/s41467-024-50620-6) | [code](https://github.com/microsoft/AI2BMD/tree/ViSNet/chignolin_data)

* **Differentiable simulation to develop molecular dynamics force fields for disordered proteins** [2024]  

Greener, Joe G.   

[Chemical Science 15.13 (2024)](https://doi.org/10.1039/D3SC05230C) | [code](https://github.com/greener-group/GB99dms)

* **Grappa--A Machine Learned Molecular Mechanics Force Field** [2024]  

Seute, Leif, Eric Hartmann, Jan Stühmer, and Frauke Gräter.   

[arXiv:2404.00050 (2024)](https://arxiv.org/abs/2404.00050) | [code](https://github.com/graeter-group/grappa)

* **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)](https://doi.org/10.1016/j.bpj.2023.04.007)  

### MD Engines-Frameworks

* [Amber](http://ambermd.org/) - A suite of biomolecular simulation programs.  

* [Gromacs](http://www.gromacs.org/) - A molecular dynamics package mainly designed for simulations of proteins, lipids and nucleic acids.

* [OpenMM](http://openmm.org/) - A toolkit for molecular simulation using high performance GPU code.  

* [CHARMM](https://www.charmm.org/) - A molecular simulation program with broad application to many-particle systems.  

* [HTMD](https://github.com/Acellera/htmd) - Programming Environment for Molecular Discovery.  

* [ACEMD](https://www.acellera.com/acemd) - The next generation molecular dynamic simulation software.  

* [NAMD](https://www.ks.uiuc.edu/Research/namd/) - A parallel molecular dynamics code for large biomolecular systems.

* [StreaMD](https://github.com/ci-lab-cz/streamd) - A tool to perform high-throughput automated molecular dynamics simulations.

* [BEMM-GEN](https://github.com/y4suda/BEMM-GEN) - A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation.

* [BioSimSpace](https://github.com/OpenBioSim/biosimspace) - An interoperable Python framework for biomolecular simulation.

### AI4MD Engines-Frameworks

* [OpenMM 8](https://github.com/openmm/openmm) - Molecular Dynamics Simulation with Machine Learning Potentials.  

* [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit) - A deep learning package for many-body potential energy representation and molecular dynamics.  

* [TorchMD](https://github.com/torchmd/torchmd) - End-To-End Molecular Dynamics (MD) Engine using PyTorch.  

* [TorchMD-NET](https://github.com/torchmd/torchmd-net) - TorchMD-NET provides state-of-the-art neural networks potentials (NNPs) and a mechanism to train them.  

* [OpenMM-Torch](https://github.com/openmm/openmm-torch) - OpenMM plugin to define forces with neural networks.  

* [AI²BMD](https://github.com/microsoft/AI2BMD) - AI-powered ab initio biomolecular dynamics simulation.  

* [NeuralMD](https://github.com/chao1224/NeuralMD) - A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics.  

### MD Trajectory Processing-Analysis

* [MDAnalysis](https://www.mdanalysis.org/) - An object-oriented Python library to analyze trajectories from molecular dynamics (MD) simulations in many popular formats.  

* [MDTraj](http://mdtraj.org/) - A python library that allows users to manipulate molecular dynamics (MD) trajectories.  

* [PyTraj](https://amber-md.github.io/pytraj/) - A Python front-end package of the popular cpptraj program.  

* [CppTraj](https://github.com/Amber-MD/cpptraj) - Biomolecular simulation trajectory/data analysis.  

* [WEDAP](https://github.com/chonglab-pitt/wedap) - A Python Package for Streamlined Plotting of Molecular Simulation Data. 

* [Melodia](https://github.com/rwmontalvao/Melodia_py) - A Python library for protein structure analysis. 

* [MDANCE](https://github.com/mqcomplab/MDANCE) - A flexible n-ary clustering package that provides a set of tools for clustering Molecular Dynamics trajectories.  

* [PENSA](https://github.com/drorlab/pensa) - A collection of python methods for exploratory analysis and comparison of biomolecular conformational ensembles.  

* **MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data** [2024]  

Ivan Gilardoni, Valerio Piomponi, Thorben Fröhlking, Giovanni Bussi.   

[	arXiv:2411.07798 (2024)](https://arxiv.org/abs/2411.07798) | [code](https://github.com/bussilab/MDRefine)  

* **NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation** [2024]  

He, Yi, Shuang Wang, Shuai Zeng, Jingxuan Zhu, Dong Xu, Weiwei Han, and Juexin Wang.   

[J. Chem. Inf. Model. (2024)](https://doi.org/10.1021/acs.jcim.4c00783) | [web](https://nrimd.luddy.indianapolis.iu.edu/)  

### Reference

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

### Visualization

* [VMD](http://www.ks.uiuc.edu/Research/vmd/) - A molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting.  

* [NGLview](https://github.com/arose/nglview) - IPython widget to interactively view molecular structures and trajectories.  

* [PyMOL](https://pymol.org/2/) - A user-sponsored molecular visualization system on an open-source foundation, maintained and distributed by Schrödinger.  

* [Avogadro](https://avogadro.cc/) - An advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.  

## AI4MD

* **A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics** [2024]  

Shengchao Liu, Weitao Du, Hannan Xu, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Divin Yan, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer Chayes.   

[arXiv:2401.15122 (2024)](https://arxiv.org/abs/2401.15122) | [code](https://github.com/chao1224/NeuralMD)  

* **Accelerating Molecular Dynamics Simulations with Quantum Accuracy by Hierarchical Classification** [2024]  

Brunken, Christoph, Sebastien Boyer, Mustafa Omar, Bakary N'tji Diallo, Karim Beguir, Nicolas Lopez Carranza, and Oliver Bent.   

[ChemRxiv. (2024)](https://doi.org/10.26434/chemrxiv-2024-20q1v) | [code](https://github.com/multiscale-modelling/humf)  

* **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)](https://openreview.net/forum?id=hrvvIOx7EM) | [code](https://github.com/ACEsuit/mace-jax)  

* **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)](https://doi.org/10.1093/bib/bbad504) | [code](https://github.com/BiomedSciAI)

* **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)](https://www.science.org/doi/10.1126/sciadv.adn4397) | [data](https://zenodo.org/records/10720941)

* **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)](https://doi.org/10.1063/5.0155600) | [code](https://github.com/deepmodeling/deepmd-kit)  

* **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)](https://doi.org/10.1016/j.cpc.2018.03.016) | [code](https://github.com/deepmodeling/deepmd-kit)  

### Neural Network Potentials

* **General-purpose machine-learned potential for 16 elemental metals and their alloys** [2024]  

Song, K., Zhao, R., Liu, J. et al.  

[Nat Commun 15, 10208 (2024)](https://doi.org/10.1038/s41467-024-54554-x) | [code](https://github.com/brucefan1983/GPUMD)    

* **Ab initio Accuracy Neural Network Potential for Drug-like Molecules** [2024]  

Yang M, Zhang D, Wang X, Zhang L, Zhu T, Wang H.  

[ChemRxiv. (2024)](https://doi.org/10.26434/chemrxiv-2024-sq8nh) | [data](https://www.aissquare.com/datasets/detail?pageType=datasets&name=Drug%28drug-like-molecule%29_DPA_v1_0&id=143)    

* **HH130: a standardized database of machine learning interatomic potentials, datasets, and its applications in the thermal transport of half-Heusler thermoelectrics** [2024]  

Yang, Yuyan, Yifei Lin, Shengnan Dai, Yifan Zhu, Jinyang Xi, Lili Xi, Xiaokun Gu, David J. Singh, Wenqing Zhang, and Jiong Yang.  

[Digital Discovery (2024)](https://doi.org/10.1039/D4DD00240G) | [data](http://www.mathub3d.net)    

* **Efficient Training of Neural Network Potentials for Chemical and Enzymatic Reactions by Continual Learning** [2024]  

Lei Y-K, Yagi K, Sugita Y.  

[ChemRxiv. (2024)](https://doi.org/10.26434/chemrxiv-2024-xkxd5)  

* **Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning** [2024]  

Allen, A.E.A., Lubbers, N., Matin, S. et al.  

[npj Comput Mater 10, 154 (2024)](https://doi.org/10.1038/s41524-024-01339-x)  

* **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)](https://doi.org/10.1038/s41524-024-01427-y) | [code](https://github.com/asharma-ms/MOF_MLP_2024)  

* **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)](https://doi.org/10.26434/chemrxiv-2024-bdfr0) | [code](https://github.com/general-molecular-simulations/so3lr)  

* **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)](https://arxiv.org/abs/2409.17852) | [code](https://github.com/compsciencelab/amaro)  

* **Revisiting Aspirin Polymorphic Stability Using a Machine Learning Potential** [2024]  

Hattori, Shinnosuke, and Qiang Zhu.  

[ACS Omega (2024)](https://doi.org/10.1021/acsomega.4c04782)  

* **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)](https://doi.org/10.1021/acs.jcim.3c02031) | [code](https://github.com/compsciencelab/ATM_benchmark/tree/main/ATM_With_NNPs)  

* **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)](https://doi.org/10.1038/s43246-024-00595-0)  

* **The Potential of Neural Network Potentials** [2024]  

Duignan, Timothy T.   

[ACS Physical Chemistry Au 4.3 (2024)](https://doi.org/10.1021/acsphyschemau.4c00004)  

* **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)](https://doi.org/10.26434/chemrxiv-2023-296ch-v2) | [code](https://github.com/isayevlab/aimnet2)  

* **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)](https://doi.org/10.1021/acs.jcim.3c00773) | [code](https://github.com/openmm/nnpops)  

* **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)](https://doi.org/10.1038/s41467-022-30687-9) | [data](https://doi.org/10.6084/m9.figshare.19658538)  

* **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)](https://doi.org/10.1038/s41467-021-24904-0)  | [code](https://github.com/isayevlab/aimnetnse)  

* **Four Generations of High-Dimensional Neural Network Potentials** [2021]  

Behler, Jorg.   

[Chemical Reviews 121.16 (2021)](https://doi.org/10.1021/acs.chemrev.0c00868)  

* **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)](https://doi.org/10.1016/j.cpc.2020.107206)  | [code](https://github.com/deepmodeling/dpgen)  

### Free Energy Perturbation

* **Comparison of Methodologies for Absolute Binding Free Energy Calculations of Ligands to Intrinsically Disordered Proteins** [2024]   

Michail Papadourakis, Zoe Cournia, Antonia S. J. S. Mey, and Julien Michel.    

  [J. Chem. Theory Comput. (2024)](https://doi.org/10.1021/acs.jctc.4c00942) | [code](https://github.com/michellab/idpabfe)    

* **FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free Energy Calculation Workflow for Drug Discovery** [2024]   

Pengfei Li,Tingting Pu ,Ye Mei.    

  [ChemRxiv. (2024)](https://doi.org/10.26434/chemrxiv-2024-tkvrh) | [code](https://github.com/freeenergylab/FEP-SPell-ABFE)    

* **Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics** [2024]   

Wei Jiang.   

  [J. Chem. Theory Comput. (2024)](https://doi.org/10.1021/acs.jctc.4c00191)  

* **Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery** [2024]   

 Qian, Runtong, Jing Xue, You Xu, and Jing Huang.   

  [J. Chem. Inf. Model. (2024)](https://doi.org/10.1021/acs.jcim.4c01024)  

* **Automated Adaptive Absolute Binding Free Energy Calculations** [2024]  

Clark, Finlay, Graeme Robb, Daniel Cole, and Julien Michel.   

[J. Chem. Theory Comput. (2024)](https://doi.org/10.1021/acs.jctc.4c00806) | [code](https://github.com/michellab/a3fe)  

* **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)](https://doi.org/10.1021/acs.jctc.4c00399) | [code](https://zenodo.org/records/12827945)  

* **The maximal and current accuracy of rigorous protein-ligand binding free energy calculations** [2023]   

Ross, G.A., Lu, C., Scarabelli, G. et al.    

  [Commun Chem 6, 222 (2023)](https://doi.org/10.1038/s42004-023-01019-9) | [code](https://github.com/schrodinger/public_binding_free_energy_benchmark)    

### Ab Initio

* **Analytical ab initio hessian from a deep learning potential for transition state optimization** [2024]  

KYuan, E.CY., Kumar, A., Guan, X. et al.  

[Nat Commun 15, 8865 (2024)](https://doi.org/10.1038/s41467-024-52481-5) | [code](https://github.com/zadorlab/sella)  

## Deep Learning-molecular conformations

* **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)](https://doi.org/10.26434/chemrxiv-2024-bdfr0) | [code](https://github.com/general-molecular-simulations/so3lr)  

* **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)](https://doi.org/10.1039/D4SC04164J) | [code](https://github.com/schnarc/SchNarc)  

* **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)](https://doi.org/10.1021/acs.jctc.1c01055) | [code](https://github.com/MiaoLab20/GLOW)  

### AlphaFold-based

* **Modeling Protein Conformations by Guiding AlphaFold2 with Distance Distributions. Application to Double Electron Electron Resonance (DEER) Spectroscopy** [2024]  

Tianqi Wu, Richard A. Stein, Te-Yu Kao, Benjamin Brown, Hassane S. Mchaourab   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.10.30.621127)  

* **AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions** [2024]  

Alireza Omidi, Mads Harder Møller, Nawar Malhis, and Jörg Gsponer.   

[bioRxiv. (2024)](https://doi.org/10.1073/pnas.2406407121) | [code](https://github.com/alirezaomidi/AFM-IDR)  

* **Harnessing AlphaFold to reveal hERG channel conformational state secrets** [2024]  

Khoa Ngo, Pei-Chi Yang, Vladimir Yarov-Yarovoy, Colleen E. Clancy, Igor Vorobyov.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.01.27.577468)  

* **AlphaFold2's training set powers its predictions of fold-switched conformations** [2024]  

Joseph W. Schafer, Lauren Porter.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.10.11.617857) | [data](https://github.com/porterll/CFold_AF2)  

* **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)](https://doi.org/10.1021/acs.jcim.4c01388) | [data](https://zenodo.org/records/13133811)  

* **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)](https://doi.org/10.1016/j.str.2024.09.001)  

* **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)](https://doi.org/10.1016/j.str.2024.09.001)  

* **AlphaFold with conformational sampling reveals the structural landscape of homorepeats** [2024]  

Bonet, David Fernandez et al.   

[Structure (2024)](https://doi.org/10.1016/j.str.2024.08.016) | [code](	https://doi.org/10.5281/zenodo.13255318)  

* **Structure prediction of alternative protein conformations** [2024]  

Bryant, P., Noé, F.   

[Nat Commun 15, 7328 (2024)](https://doi.org/10.1038/s41467-024-51507-2) | [code](https://github.com/patrickbryant1/Cfold)  

* **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)](https://doi.org/10.1038/s41467-024-51801-z) | [code](https://github.com/ncbi/AF2_benchmark)  

* **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)](https://doi.org/10.1073/pnas.2410662121)  

* **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)](https://doi.org/10.1101/2024.08.16.608358)  

* **A resource for comparing AF-Cluster and other AlphaFold2 sampling methods** [2024]  

Hannah K Wayment-Steele, Sergey Ovchinnikov, Lucy Colwell, Dorothee Kern.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.07.29.605333)  

* **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)](https://doi.org/10.1021/acs.jpcb.4c02726)  

* **AFsample2: Predicting multiple conformations and ensembles with AlphaFold2** [2024]  

Yogesh Kalakoti, Björn Wallner.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.05.28.596195) | [code](https://github.com/iamysk/AFsample2/)  

* **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)](https://doi.org/10.1101/2024.05.17.594786) | [code](https://zenodo.org/records/11204773)

* **Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE** [2024]  

Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary.   

[	arXiv:2404.07102 (2024)](https://arxiv.org/abs/2404.07102)  

* **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)](https://doi.org/10.1038/s41467-024-46715-9) | [code](https://github.com/GMdSilva/gms_natcomms_1705932980_data)

* **AlphaFold Meets Flow Matching for Generating Protein Ensembles** [2024]  

Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.   

[arXiv:2402.04845 (2024)](https://arxiv.org/abs/2402.04845) | [code](https://github.com/bjing2016/alphaflow)

* **Predicting multiple conformations via sequence clustering and AlphaFold2** [2024]  

Wayment-Steele, H.K., Ojoawo, A., Otten, R. et al.   

[Nature 625, 832–839 (2024)](https://doi.org/10.1038/s41586-023-06832-9) | [code](https://github.com/HWaymentSteele/AF_Cluster)

* **AlphaFold2-RAVE: From Sequence to Boltzmann Ranking** [2023]  

Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, and Pratyush Tiwary.   

[J. Chem. Theory Comput. (2023)](https://pubs.acs.org/doi/10.1021/acs.jctc.3c00290)) | [code](https://github.com/tiwarylab/alphafold2rave)

* **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)](https://doi.org/10.1093/bioadv/vbad129)) | [code](https://github.com/Harmonic-Discovery/AF2-kinase-conformational-landscape)

* **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)](https://doi.org/10.1101/2023.08.31.555779) | [code](https://github.com/schlessinger-lab/af2_kinase_conformations/)

* **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)](https://elifesciences.org/articles/75751) | [code](https://github.com/delalamo/af2_conformations)

### GNN-based

* **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)](https://openreview.net/forum?id=or4tArwd5a) | [code](https://openreview.net/forum?id=or4tArwd5a)

* **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)](https://doi.org/10.1101/2024.03.11.584426) | [code](https://github.com/DS00HY/RevGraphVamp)

### 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)](https://doi.org/10.1038/s41467-020-18959-8) | [code](https://github.com/tiwarylab/LSTM-predict-MD)

### 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.   

[J. Chem. Theory Comput. (2024)](https://doi.org/10.1021/acs.jctc.4c00255) | [bioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581708) | [code](https://github.com/AspirinCode/AlphaPPImd)

* **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)](https://pubs.acs.org/doi/full/10.1021/acs.jpcb.3c08195) | [code](https://github.com/rotskoff-group/transformer-backmapping)

* **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)](https://arxiv.org/abs/2206.04683) | [code](https://github.com/dgattiwsu/MD_without_molecules)

### VAE-based

* **Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning** [2024]  

Talant Ruzmetov, Ta I Hung, Saisri Padmaja Jonnalagedda, Si-han Chen, Parisa Fasihianifard, Zhefeng Guo, Bir Bhanu, Chia-en A. Chang.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.05.05.592587) | [data](https://github.com/chang-group/ICoN)  

* **Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy** [2024]  

Hu, Yao, Hao Yang, Mingwei Li, Zhicheng Zhong, Yongqi Zhou, Fang Bai, and Qian Wang.   

[Advanced Science (2024)](https://doi.org/10.1002/advs.202400884) | [code](https://github.com/qwang897/PATHpre)  

* **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)](https://doi.org/10.1021/acs.jcim.4c00189) | [data](https://zenodo.org/records/10500539)

* **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)](https://pubs.acs.org/doi/10.1021/acs.jctc.3c01057)  

* **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)](https://academic.oup.com/bib/article/25/1/bbad429/7453435) | [code](https://github.com/Junjie-Zhu/Phanto-IDP)

* **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)](https://www.mdpi.com/1422-0067/24/8/6896) | [code](https://github.com/Junjie-Zhu/VAE)

* **Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence** [2023]  

  Su, Zhaoqian, Kalyani Dhusia, and Yinghao Wu.  

  [bioRxiv (2023)](https://doi.org/10.1101/2023.09.08.556812)  

* **Artificial intelligence guided conformational mining of intrinsically disordered proteins** [2022]  

Gupta, A., Dey, S., Hicks, A. et al.   

[Commun Biol 5, 610 (2022)](https://doi.org/10.1038/s42003-022-03562-y) | [code](https://github.com/aaayushg/generative_IDPs)

* **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)](https://doi.org/10.1021/acs.jcim.2c01213) | [code](https://github.com/smu-tao-group/LAST)

* **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)](https://arxiv.org/abs/2206.04683) | [code](https://github.com/dgattiwsu/MD_without_molecules)

* **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)](https://openreview.net/forum?id=LxhlyKH6VP)

* **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)](https://www.frontiersin.org/articles/10.3389/fmolb.2021.781635/full) | [code](https://github.com/smu-tao-group/protein-VAE)

### 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)](https://doi.org/10.1038/s41467-023-36443-x) | [code](https://github.com/feiglab/idpgan)

* **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)](https://arxiv.org/abs/2206.04683) | [code](https://github.com/dgattiwsu/MD_without_molecules)

### Flow-based

* **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)](https://openreview.net/forum?id=LbwM4VCDUU) | [code](https://github.com/bjing2016/mdgen)  

* **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)](https://arxiv.org/abs/2405.00751)  

* **AlphaFold Meets Flow Matching for Generating Protein Ensembles** [2024]  

Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.   

[arXiv:2402.04845 (2024)](https://arxiv.org/abs/2402.04845) | [code](https://github.com/bjing2016/alphaflow)  

### Diffusion-based

* **Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDB** [2024]  

Yike Tang, Mendi Yu, Ganggang Bai, Xinjun Li, Yanyan Xu, Buyong Ma.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.06.27.600251)  

* **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)](https://arxiv.org/abs/2408.12419)  

* **Generating Multi-state Conformations of P-type ATPases with a Diffusion Model** [2024]  

Jingtian Xu, Yong Wang.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.08.07.607107) | [code](https://github.com/yongwangCPH/papers/tree/main/2024/PtypeATPaseGeneration)

* **Transferable deep generative modeling of intrinsically disordered protein conformations** [2024]  

Abdin, O., Kim, P.M.   

[PLOS Computational Biology 20.5 (2024)](https://doi.org/10.1371/journal.pcbi.1012144) | [code](https://github.com/giacomo-janson/idpsam)  

* **Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion** [2024]  

Janson, Giacomo, and Michael Feig.   

[Nat Mach Intell 6, 775–786 (2024)](https://doi.org/10.1038/s42256-024-00860-4) | [code](https://gitlab.com/oabdin/pepflow)

* **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)](https://doi.org/10.1021/acs.jcim.4c00928) | [bioRxiv (2024)](https://doi.org/10.1101/2024.05.20.594916) | [code](https://github.com/PKUfjh/Uni-Fold/tree/ufconf)    

* **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)](https://arxiv.org/abs/2305.19800) | [code](https://github.com/genentech/ringer)  

### 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)](https://openreview.net/forum?id=C4BikKsgmK) | [code](https://github.com/lujiarui/Str2Str)

* **Score-based enhanced sampling for protein molecular dynamics** [2023]  

Lu, Jiarui, Bozitao Zhong, and Jian Tang.   

[arXiv:2306.03117 (2023)](https://arxiv.org/abs/2306.03117) | [code](https://github.com/lujiarui/Str2Str)

### Energy-based

* **Energy-based models for atomic-resolution protein conformations** [2020]  

Du, Yilun, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives.   

[ICLR (2020)](https://openreview.net/forum?id=S1e_9xrFvS) | [code](https://github.com/facebookresearch/protein-ebm)

### Bayesian-based

* **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)](https://doi.org/10.1093/bioinformatics/btae484)

* **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)](https://doi.org/10.1101/2023.03.25.534210) | [code](https://github.com/MiaoLab20/DBMD/)

* **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)](https://doi.org/10.1101/2022.07.29.501943) | [code](https://github.com/bouralab/DeepUrfold)

### 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)](https://doi.org/10.1021/acs.jctc.3c00040) | [code](https://github.com/ShuklaGroup/MaxEntVAMPNet)  

### LLM-MD

* **Structure Language Models for Protein Conformation Generation** [2024]  

Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Chence Shi, Hongyu Guo, Yoshua Bengio, Jian Tang.   

[	arXiv:2410.18403 (2024)](https://arxiv.org/abs/2410.18403) | [code](https://github.com/lujiarui/esmdiff)  

* **SeaMoon: Prediction of molecular motions based on language models** [2024]  

Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.09.23.614585) | [code](https://github.com/PhyloSofS-Team//seamoon)  

* **Molecular simulation with an LLM-agent** [2024]  

MD-Agent is a LLM-agent based toolset for Molecular Dynamics.  

[code](https://github.com/ur-whitelab/md-agent)

## Molecular conformational ensembles by methods

### Small molecule conformational ensembles

* **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)](https://doi.org/10.1038/s41467-023-44629-6) |  [code](https://github.com/seonghann/tsdiff)

* **Physics-informed generative model for drug-like molecule conformers** [2024]   

David C. Williams, Neil Imana.   

[	arXiv:2403.07925. (2024)](https://arxiv.org/abs/2403.07925v1) |  [code](https://github.com/nobiastx/diffusion-conformer)   

* **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)](https://doi.org/10.1021/acs.jcim.3c00989) |  [code](https://github.com/insilicomedicine/COSMIC)  

* **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)](https://doi.org/10.1016/j.compchemeng.2024.108622) |  [code](https://github.com/m1k2zoo/2D-3DConformerGNN) 

* **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)](https://openreview.net/forum?id=pwYCCq4xAf) 

* **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))](https://arxiv.org/abs/2310.04915) 

* **Molecular Conformation Generation via Shifting Scores** [2023]   

Zhou, Zihan, Ruiying Liu, Chaolong Ying, Ruimao Zhang, and Tianshu Yu.   

[arXiv:2309.09985 (2023)](https://arxiv.org/abs/2309.09985) 

* **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)](https://arxiv.org/abs/2308.00237) 

* **Prediction of Molecular Conformation Using Deep Generative Neural Networks** [2023]   

Xu, Congsheng, Yi Lu, Xiaomei Deng, and Peiyuan Yu.   

[Chinese Journal of Chemistry(2023)](https://doi.org/10.1002/cjoc.202300269)

* **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)](https://openreview.net/forum?id=kFiMXnLH9x) |  [code](https://github.com/SXKDZ/MARCEL) 

* **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)](https://doi.org/10.1021/acs.jpcb.3c05284) 

* **Torsional diffusion for molecular conformer generation** [2022]   

Jing, Bowen, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.   

[NeurIPS. (2022)](https://proceedings.neurips.cc/paper_files/paper/2022/hash/994545b2308bbbbc97e3e687ea9e464f-Abstract-Conference.html) |  [code](https://github.com/gcorso/torsional-diffusionf) 

* **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)](https://openreview.net/forum?id=PzcvxEMzvQC) |  [code](https://github.com/MinkaiXu/GeoDiff) 

* **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)](https://doi.org/10.1002/jcc.26984) |  [code](https://github.com/ZimmermanGroup/conformer-rl) 

* **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)](https://openreview.net/forum?id=7QfLW-XZTl) |  [code](https://github.com/guanjq/confopt_officialf) 

* **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)](http://proceedings.mlr.press/v139/xu21f.html) |  [code](https://github.com/MinkaiXu/ConfVAE-ICML21)  

### RNA conformational ensembles

* **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)](https://doi.org/10.1021/acs.jctc.4c00773)  

* **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)](https://doi.org/10.1038/s41597-024-03698-y) | [code](https://github.com/sbottaro/rr) | [data](https://github.com/sbottaro/tetranucleotides_data)

### Peptide conformational ensembles

* **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)](https://doi.org/10.1038/s41597-024-03698-y) | [code](https://github.com/Genentech/cremp)

* **Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion** [2024]  

Abdin, O., Kim, P.M.   

[Nat Mach Intell 6, 775–786 (2024)](https://doi.org/10.1038/s42256-024-00860-4) | [code](https://gitlab.com/oabdin/pepflow)

* **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)](https://arxiv.org/abs/2305.19800) | [code](https://github.com/genentech/ringer)  

### Protein conformational ensembles

* **Fast Sampling of Protein Conformational Dynamics** [2024]  

Michael A. Sauer, Souvik Mondal, Brandon Neff, Sthitadhi Maiti, Matthias Heyden.   

[	arXiv:2411.08154 (2024)](https://arxiv.org/abs/2411.08154)  

* **Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning** [2024]  

Talant Ruzmetov, Ta I Hung, Saisri Padmaja Jonnalagedda, Si-han Chen, Parisa Fasihianifard, Zhefeng Guo, Bir Bhanu, Chia-en A. Chang.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.05.05.592587) | [data](https://github.com/chang-group/ICoN)  

* **AlphaFold2's training set powers its predictions of fold-switched conformations** [2024]  

Joseph W. Schafer, Lauren Porter.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.10.11.617857) | [data](https://github.com/porterll/CFold_AF2)  

* **Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy** [2024]  

Hu, Yao, Hao Yang, Mingwei Li, Zhicheng Zhong, Yongqi Zhou, Fang Bai, and Qian Wang.   

[Advanced Science (2024)](https://doi.org/10.1002/advs.202400884) | [code](https://github.com/qwang897/PATHpre)  

* **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)](https://arxiv.org/abs/2409.17852) | [code](https://github.com/compsciencelab/amaro)  

* **Conformations of KRAS4B Affected by Its Partner Binding and G12C Mutation: Insights from GaMD Trajectory-Image Transformation-Based Deep Learning** [2024]  

Chen, Jianzhong, Jian Wang, Wanchun Yang, Lu Zhao, and Guodong Hu.   

[J. Chem. Inf. Model. (2024)](https://doi.org/10.1021/acs.jcim.4c01174) | [code](https://github.com/jianzhong70/Visualiztion-for-MD-trajectory-analysis)  

* **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)](https://doi.org/10.1016/j.str.2024.09.001)  

* **Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies** [2024]  

Ellaway, J. I., Anyango, S., Nair, S., Zaki, H. A., Nadzirin, N., Powell, H. R., ... & Velankar, S.   

[Structural Dynamics (2024)](https://doi.org/10.1063/4.0000251)   

* **AlphaFold with conformational sampling reveals the structural landscape of homorepeats** [2024]  

Bonet, David Fernandez et al.   

[Structure (2024)](https://doi.org/10.1016/j.str.2024.08.016) | [code](	https://doi.org/10.5281/zenodo.13255318)  

* **Structure prediction of alternative protein conformations** [2024]  

Bryant, P., Noé, F.   

[Nat Commun 15, 7328 (2024)](https://doi.org/10.1038/s41467-024-51507-2) | [code](https://github.com/patrickbryant1/Cfold)  

* **Deep learning guided design of dynamic proteins** [2024]  

Amy B. Guo, Deniz Akpinaroglu, Mark J.S. Kelly, Tanja Kortemme.   

[bioRxiv. (2024)](https://doi.org/10.1101/2024.07.17.603962)  

* **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)](https://doi.org/10.1038/s41467-024-51801-z) | [code](https://github.com/ncbi/AF2_benchmark)  

* **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)](https://arxiv.org/abs/2408.12419)  

* **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)](https://doi.org/10.1073/pnas.2410662121)  

* **A resource for comparing AF-Cluster and other AlphaFold2 sampling methods** [2024]  

Hannah K Wayment-Steele, Sergey Ovchinnikov, Lucy Colwell, Dorothee Kern.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.07.29.605333) 

* **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)](https://doi.org/10.1021/acs.jpcb.4c02726)  

* **Transferable deep generative modeling of intrinsically disordered protein conformations** [2024]  

Abdin, O., Kim, P.M.   

[PLOS Computational Biology 20.5 (2024)](https://doi.org/10.1371/journal.pcbi.1012144) | [code](https://github.com/giacomo-janson/idpsam)  

* **AFsample2: Predicting multiple conformations and ensembles with AlphaFold2** [2024]  

Yogesh Kalakoti, Björn Wallner.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.05.28.596195) | [code](https://github.com/iamysk/AFsample2/)

* **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)](https://doi.org/10.1101/2024.05.17.594786) | [code](https://zenodo.org/records/11204773)

* **Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE** [2024]  

Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary.   

[	arXiv:2404.07102 (2024)](https://arxiv.org/abs/2404.07102)  

* **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)](https://doi.org/10.1038/s41467-024-46715-9) | [code](https://github.com/GMdSilva/gms_natcomms_1705932980_data)

* **AlphaFold Meets Flow Matching for Generating Protein Ensembles** [2024]  

Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.   

[arXiv:2402.04845 (2024)](https://arxiv.org/abs/2402.04845) | [code](https://github.com/bjing2016/alphaflow)

* **Predicting multiple conformations via sequence clustering and AlphaFold2** [2024]  

Wayment-Steele, H.K., Ojoawo, A., Otten, R. et al.   

[Nature 625, 832–839 (2024)](https://doi.org/10.1038/s41586-023-06832-9) | [code](https://github.com/HWaymentSteele/AF_Cluster)

* **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)](https://pubs.acs.org/doi/full/10.1021/acs.jpcb.3c08195) | [code](https://github.com/rotskoff-group/transformer-backmapping)

* **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)](https://arxiv.org/abs/2405.00751)  

* **AlphaFold Meets Flow Matching for Generating Protein Ensembles** [2024]  

Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.   

[arXiv:2402.04845 (2024)](https://arxiv.org/abs/2402.04845) | [code](https://github.com/bjing2016/alphaflow)

* **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)](https://openreview.net/forum?id=hrvvIOx7EM) | [code](https://github.com/ACEsuit/mace-jax)  

* **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)](https://doi.org/10.1021/acs.jcim.4c00189) | [data](https://zenodo.org/records/10500539)

* **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)](https://pubs.acs.org/doi/10.1021/acs.jctc.3c01057)

* **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)](https://academic.oup.com/bib/article/25/1/bbad429/7453435) | [code](https://github.com/Junjie-Zhu/Phanto-IDP)

* **Str2str: A score-based framework for zero-shot protein conformation sampling** [2024]  

Lu, Jiarui, Bozitao Zhong, Zuobai Zhang, and Jian Tang.   

[ICLR (2024)](https://openreview.net/forum?id=C4BikKsgmK) | [code](https://github.com/lujiarui/Str2Str)

* **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)](https://doi.org/10.1093/bioinformatics/btae484)

* **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)](https://doi.org/10.1101/2023.03.25.534210) | [code](https://github.com/MiaoLab20/DBMD/)

* **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)](https://doi.org/10.1101/2022.07.29.501943) | [code](https://github.com/bouralab/DeepUrfold)

* **Score-based enhanced sampling for protein molecular dynamics** [2023]  

Lu, Jiarui, Bozitao Zhong, and Jian Tang.   

[arXiv:2306.03117 (2023)](https://arxiv.org/abs/2306.03117) | [code](https://github.com/lujiarui/Str2Str)

* **AlphaFold2-RAVE: From Sequence to Boltzmann Ranking** [2023]  

Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, and Pratyush Tiwary.   

[J. Chem. Theory Comput. (2023)](https://pubs.acs.org/doi/10.1021/acs.jctc.3c00290)) | [code](https://github.com/tiwarylab/alphafold2rave)

* **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)](https://doi.org/10.1093/bioadv/vbad129)) | [code](https://github.com/Harmonic-Discovery/AF2-kinase-conformational-landscape)

* **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)](https://doi.org/10.1101/2023.08.31.555779) | [code](https://github.com/schlessinger-lab/af2_kinase_conformations/)

* **Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets** [2023]  

Kleiman, Diego E., and Diwakar Shukla.   

[J. Chem. Theory Comput. (2023)](https://doi.org/10.1021/acs.jctc.3c00040) | [code](https://github.com/ShuklaGroup/MaxEntVAMPNet)

* **Direct generation of protein conformational ensembles via machine learning** [2023]  

Janson, G., Valdes-Garcia, G., Heo, L. et al.   

[Nat Commun 14, 774 (2023)](https://doi.org/10.1038/s41467-023-36443-x) | [code](https://github.com/feiglab/idpgan)

* **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)](https://www.mdpi.com/1422-0067/24/8/6896) | [code](https://github.com/Junjie-Zhu/VAE)

* **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)](https://arxiv.org/abs/2206.04683) | [code](https://github.com/dgattiwsu/MD_without_molecules)

* **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)](https://elifesciences.org/articles/75751) | [code](https://github.com/delalamo/af2_conformations)  

* **Artificial intelligence guided conformational mining of intrinsically disordered proteins** [2022]  

Gupta, A., Dey, S., Hicks, A. et al.   

[Commun Biol 5, 610 (2022)](https://doi.org/10.1038/s42003-022-03562-y) | [code](https://github.com/aaayushg/generative_IDPs)

* **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)](https://doi.org/10.1021/acs.jcim.2c01213) | [code](https://github.com/smu-tao-group/LAST)

* **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)](https://arxiv.org/abs/2206.04683) | [code](https://github.com/dgattiwsu/MD_without_molecules)

* **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)](https://openreview.net/forum?id=LxhlyKH6VP)

* **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)](https://www.frontiersin.org/articles/10.3389/fmolb.2021.781635/full) | [code](https://github.com/smu-tao-group/protein-VAE)

* **Energy-based models for atomic-resolution protein conformations** [2020]  

Du, Yilun, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives.   

[ICLR (2020)](https://openreview.net/forum?id=S1e_9xrFvS) | [code](https://github.com/facebookresearch/protein-ebm)

### Enzymes conformational ensembles

* **Generating Multi-state Conformations of P-type ATPases with a Diffusion Model** [2024]  

Jingtian Xu, Yong Wang.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.08.07.607107) | [code](https://github.com/yongwangCPH/papers/tree/main/2024/PtypeATPaseGeneration)

* **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)](https://doi.org/10.1021/acs.jcim.4c00189) | [data](https://zenodo.org/records/10500539)

### 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)](https://openreview.net/forum?id=or4tArwd5a) | [code](https://openreview.net/forum?id=or4tArwd5a)

### Ligand-Protein conformational ensembles

* **A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics** [2024]  

Shengchao Liu, Weitao Du, Hannan Xu, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Divin Yan, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer Chayes.   

[arXiv:2401.15122 (2024)](https://arxiv.org/abs/2401.15122) | [code](https://github.com/chao1224/NeuralMD)  

* **Modeling protein-small molecule conformational ensembles with ChemNet** [2024]  

Ivan Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J. Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David Baker.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.09.25.614868)  

* **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)](https://doi.org/10.1038/s43588-024-00627-2) | [code](https://github.com/t7morgen/misato-dataset)  

* **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)](https://doi.org/10.1021/acs.jcim.3c02031) | [code](https://github.com/compsciencelab/ATM_benchmark/tree/main/ATM_With_NNPs)  

* **Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction** [2024]  

Poziemski, Jakub, Artur Yurkevych, and Pawel Siedlecki.   

[chemrxiv-2024-dxv36 (2024)](https://doi.org/10.26434/chemrxiv-2024-dxv36) | [code](https://github.com/JPoziemski/md_for_affinity_prediction)  

* **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)](https://doi.org/10.1002/advs.202203796) | [code](https://github.com/smiles724/ProtMD)  

### PPI conformational ensembles

* **Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD** [2024]  

D’Arrigo, G., Kokh, D.B., Nunes-Alves, A. et al.   

[Commun Biol 7, 1159 (2024)](https://doi.org/10.1038/s42003-024-06880-5) | [code](https://github.com/HITS-MCM/tauRAMD)  

* **Quantifying conformational changes in the TCR:pMHC-I binding interface** [2024]  

Benjamin McMaster, Christopher Thorpe, Jamie Rossjohn, Charlotte M. Deane, Hashem Koohy.   

[bioRxiv (2024)](https://doi.org/10.1101/2024.08.13.607715) | [code](https://github.com/benjiemc/tcr-pmhc-interface-analysis)  

* **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)](https://doi.org/10.1021/acs.jctc.4c00255) | [bioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581708) | [code](https://github.com/AspirinCode/AlphaPPImd)

* **Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence** [2023]  

  Su, Zhaoqian, Kalyani Dhusia, and Yinghao Wu.  

  [bioRxiv (2023)](https://doi.org/10.1101/2023.09.08.556812)  

* **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)](https://doi.org/10.1093/bib/bbad504) | [code](https://github.com/BiomedSciAI)  

### 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)](https://doi.org/10.1101/2024.08.01.606277)  

### Antibody-Protein conformational ensembles

* **Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody–Protein Complexes** [2024]  

A. Clay Richard, Robert J. Pantazes.   

[Proteins. (2024)](https://doi.org/10.1002/prot.26773)  

### Material ensembles

* **General-purpose machine-learned potential for 16 elemental metals and their alloys** [2024]  

Song, K., Zhao, R., Liu, J. et al.  

[Nat Commun 15, 10208 (2024)](https://doi.org/10.1038/s41467-024-54554-x) | [code](https://github.com/brucefan1983/GPUMD)    

* **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)](https://doi.org/10.1038/s41524-024-01427-y) | [code](https://github.com/asharma-ms/MOF_MLP_2024)  

* **Generative AI model trained by molecular dynamics for rapid mechanical design of architected graphene** [2024]  

Milad Masrouri, Kamalendu Paul, Zhao Qin.   

[Extreme Mechanics Letters (2024)](https://doi.org/10.1016/j.eml.2024.102230)  

* **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)](https://doi.org/10.1038/s41557-024-01593-y) | [data](https://zenodo.org/records/11965260)  

* **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)](https://doi.org/10.1038/s43246-024-00595-0)