List of protein (enzymes and PPIs) conformations and molecular dynamics using generative artificial intelligence and deep learning
List of protein ( enzymes and PPIs) conformations and molecular dynamics (MD) using generative artificial intelligence and deep learning
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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)
MMolearn
a Python package streamlining the design of generative models of biomolecular dynamics
https://github.com/LumosBio/MolData
https://github.com/ipudu/awesome-molecular-dynamics
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
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE [2024]
Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary.
arXiv:2404.07102 (2024)
High-throughput prediction of protein conformational distributions with subsampled AlphaFold2 [2024]
Monteiro da Silva, G., Cui, J.Y., Dalgarno, D.C. et al.
Nat Commun 15, 2464 (2024) | code
AlphaFold Meets Flow Matching for Generating Protein Ensembles [2024]
Jing, Bowen, Bonnie Berger, and Tommi Jaakkola.
arXiv:2402.04845 (2024) | code
Predicting multiple conformations via sequence clustering and AlphaFold2 [2024]
Wayment-Steele, H.K., Ojoawo, A., Otten, R. et al.
Nature 625, 832–839 (2024) | code
Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures [2023]
Herrington, Noah B., David Stein, Yan Chak Li, Gaurav Pandey, and Avner Schlessinger.
bioRxiv (2023) | code
Sampling alternative conformational states of transporters and receptors with AlphaFold2 [2022]
Del Alamo, Diego, Davide Sala, Hassane S. Mchaourab, and Jens Meiler.
Elife 11 (2022) | code
Exploring the conformational ensembles of protein-protein complex with transformer-based generative model [2024]
Wang, Jianmin, Xun Wang, Yanyi Chu, Chunyan Li, Xue Li, Xiangyu Meng, Yitian Fang, Kyoung Tai No, Jiashun Mao, and Xiangxiang Zeng.
bioRxiv (2024) | code
Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers [2024]
Chennakesavalu, Shriram, and Grant M. Rotskoff.
The Journal of Physical Chemistry B (2024) | code
Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [2022]
Schwing, Gregory, Luigi L. Palese, Ariel Fernández, Loren Schwiebert, and Domenico L. Gatti.
arXiv:2206.04683 (2022) | code
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
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
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
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