Alphafold-latest🔥 and RFAA🔥 have revolutionize the scope of docking. Previous work was focused on modeling different components separately, but these two studies have used a single model to simultaneously model all biomolecular interactions. Here is a curated paper list containing all kinds of deep learning-based docking, covering Protein-Ligand Docking, Protein-Protein Docking, Protein-Nucleic Acid Docking, and Covalent Docking. Additionally, we refer to works capable of handling various types of docking scenarios simultaneously as 'Versatile Docking'. Future work will encompass tools, datasets, scoring function design, and other relvant topics. Within each category, entries are listed in reverse chronological order, with the most recent first. If a paper has multiple versions, we reference the initial publication date. The following badges are used for according purpose:
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- Versatile Docking
- Protein-Ligand Docking
- Protein-Protein Docking
- Protein-Nucleic Acid Docking
- Covalent Docking
- Survey
- Traditional Docking Tools
🔥A glimpse of the next generation of AlphaFold
Google DeepMind AlphaFold team and Isomorphic Labs team
News, October 2023
🔥Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, Ryan McHugh, Dionne Vafeados, Xinting Li, George A Sutherland, Andrew Hitchcock, C Neil Hunter, Minkyung Baek, Frank DiMaio, David Baker
Science, March 2024
CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training
Heng Cai, Chao Shen, Tianye Jian, Xujun Zhang, Tong Chen, Xiaoqi Han, Zhuo Yang, Wei Dang, Chang-Yu Hsieh, Yu Kang, Peichen Pan, Xiangyang Ji, Jianfei Song, Tingjun Hou and Yafeng Deng
Chemical Science, December 2023
GeoDirDock: Guiding Docking Along Geodesic Paths
Raúl Miñán, Javier Gallardo, Álvaro Ciudad, Alexis Molina
Preprint, April 2024
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
Houtim Lai, Longyue Wang, Ruiyuan Qian, Geyan Ye, Juhong Huang, Fandi Wu, Fang Wu, Xiangxiang Zeng, Wei Liu
Preprint, April 2024
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Kaiyuan Gao, Qizhi Pei, Jinhua Zhu, Tao Qin, Kun He, Lijun Wu
Preprint, April 2024
ArtiDock: fast and accurate machine learning approach to protein-ligand docking based on multimodal data augmentation
Taras Voitsitskyi, Semen Yesylevskyy, Volodymyr Bdzhola, Roman Stratiichuk, Ihor Koleiev, Zakhar Ostrovsky, Volodymyr Vozniak, Ivan Khropachov, Pavlo Henitsoi, Leonid Popryho, Roman Zhytar, Alan Nafiiev, Serhii Starosyla
Preprint, March 2024
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan. Z. Li
Preprint, February 2024
PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking
Runze Zhang, Xinyu Jiang, Duanhua Cao, Jie Yu, Mingan Chen, Zhehuan Fan, Xiangtai Kong, Jiacheng Xiong, Zimei Zhang, Wei Zhang, Shengkun Ni, Yitian Wang, Shenghua Gao, Mingyue Zheng
Preprint, February 2024
State-specific protein–ligand complex structure prediction with a multiscale deep generative model
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller II, Animashree Anandkumar
Nature Machine Intelligence, February 2024
DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Wei Lu, Jixian Zhang, Huang Weifeng, Ziqiao Zhang, Chengtao Li, Shuangjia Zheng
Nature Communications, February 2024
Deep confident steps to new pockets: strategies for docking generalization
Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola
ICLR, Feburary 2024
(NeuralMD) A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer Chayes
Preprint, January 2024
(DeltaDock) Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Jiaxian Yan, Zaixi Zhang, Kai Zhang, Qi Liu
Preprint, December 2023
DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking
Jintao Zhu, Zhonghui Gu, Jianfeng Pei, Luhua Lai
Preprint, November 2023
Structure prediction of protein-ligand complexes from sequence information with Umol
Patrick Bryant, Atharva Kelkar, Andrea Guljas, Cecilia Clementi, Frank Noé
Preprint, November 2023
(FlexPose) Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning
Tiejun Dong, Ziduo Yang, Jun Zhou, and Calvin Yu-Chian Chen
JCTC, November 2023
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M. Morris, Charlotte M. Deane
Preprint, October 2023.
FABind: Fast and Accurate Protein-Ligand Binding
Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
NeurIPS, September 2023
Efficient and accurate large library ligand docking with KarmaDock
Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, Yafeng Deng, Furui Liu, Tianyue Wang, Hongyan Du, Langcheng Wang, Peichen Pan, Guangyong Chen, Chang-Yu Hsieh, Tingjun Hou
Nature Computational Science, September 2023
(EDM-Dock) Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility
Matthew R. Masters, Amr H. Mahmoud, Yao Wei, and Markus A. Lill
JCIM, March 2023
Do deep learning models really outperform traditional approaches in molecular docking?
Yuejiang Yu, Shuqi Lu, Zhifeng Gao, Hang Zheng, Guolin Ke
ICLR workshop MLDD, March 2023
🔥DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
ICLR, Feburary 2023
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke
ICLR, Feburary 2023
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang, Huiyu Cai, Chence Shi, Jian Tang
ICLR, Feburary 2023
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, Shuangjia Zheng
NeurIPS, November 2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola
ICML, July 2022
Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data
Wei Zheng, Qiqige Wuyun, Yang Li, Chengxin Zhang, P. Lydia Freddolino, Yang Zhang
Nature Methods, January 2024
Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model
Huaijin Wu, Wei Liu, Yatao Bian, Jiaxiang Wu, Nianzu Yang, Junchi Yan
ICLR, 2024
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction
Ziyang Yu, Wenbing Huang, Yang Liu
ICLR, 2024
(GeoDock) Flexible protein–protein docking with a multitrack iterative transformer
Lee-Shin Chu, Jeffrey A. Ruffolo, Ameya Harmalkar, Jeffrey J. Gray
Protein Science, December 2023
Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15
Jian Liu, Zhiye Guo, Tianqi Wu, Rajashree Roy, Farhan Quadir, Chen Chen, Jianlin Cheng
Communications Biology, November 2023
DockGame: Cooperative Games for Multimeric Rigid Protein Docking
Vignesh Ram Somnath, Pier Giuseppe Sessa, Maria Rodriguez Martinez, Andreas Krause
Preprint, October 2023
Diffdock-pp: Rigid protein-protein docking with diffusion models
Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stärk, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola
ICLR workshop MLDD, March 2023
Deep Learning for Flexible and Site-Specific Protein Docking and Design
Matthew McPartlon, Jinbo Xu
BioRxiv, April 2023
Physics-informed deep neural network for rigid-body protein docking
Freyr Sverrisson, Jean Feydy, Joshua Southern, Michael M Bronstein, Bruno E Correia
ICLR workshop MLDD, April 2022
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
ICLR, January 2022
🔥Protein complex prediction with AlphaFold-Multimer
Richard Evans, Michael O’Neill, A. Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Zídek, Russ Bates, Sam Blackwell, Jason Yim, O. Ronneberger, S. Bodenstein, Michal Zielinski, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, J. Jumper, D. Hassabis
BioRxiv, October 2021
🔥Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA
Minkyung Baek, Ryan McHugh, Ivan Anishchenko, Hanlun Jiang, David Baker, Frank DiMaio
Nature Methods, November 2023
EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks
Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Sumit Tarafder, Debswapna Bhattacharya
BioRxiv, September 2023
Evaluating native-like structures of RNA-protein complexes through the deep learning method
Chengwei Zeng, Yiren Jian, Soroush Vosoughi, Chen Zeng, Yunjie Zhao
Nature Communications, February 2023
Cov_DOX: A Method for Structure Prediction of Covalent Protein–Ligand Bindings
Lin Wei, Yaru Chen, Jiaqi Liu, Li Rao, Yanliang Ren, Xin Xu, Jian Wan
Journal of Medicinal Chemistry, March 2022
CovPDB: a high-resolution coverage of the covalent protein–ligand interactome
Mingjie Gao, Aurelien F. A. Moumbock, Ammar Qaseem, Qianqing Xu, Stefan Gunther
Nucleic Acids Research, September 2021
Fragment-based covalent ligand discovery
Wenchao Lu, Milka Kostic, Tinghu Zhang, Jianwei Che, Matthew P. Patricelli, Lyn H. Jones, Edward T. Chouchaniae, Nathanael S. Gray
RSC Chemical Biology, February 2021
Covalent docking of large libraries for the discovery of chemical probes
Nir London, Rand M Miller, Shyam Krishnan, Kenji Uchida, John J Irwin, Oliv Eidam, Lucie Gibold, Peter Cimermančič, Richard Bonnet, Brian K Shoichet, Jack Taunton
Nature Chemical Biology, September 2014
Docking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring
Kai Zhu, Kenneth W. Borrelli, Jeremy R. Greenwood, Tyler Day, Robert Abel, Ramy S. Farid, and Edward Harder
Journal of Chemical Information and Modeling, June 2014
CovalentDock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints
Xuchang Ouyang, Shuo Zhou, Chinh Tran To Su, Zemei Ge, Runtao Li, Chee Keong Kwoh
Journal of Computational Chemistry, February 2013
Machine-learning methods for ligand–protein molecular docking
Kevin Crampon, Alexis Giorkallos, Myrtille Deldossi, Stéphanie Baud, Luiz Angelo Steffenel
Drug Discovery Today, January 2022
A practical guide to large-scale docking
Brian J. Bender, Stefan Gahbauer, Andreas Luttens, Jiankun Lyu, Chase M. Webb, Reed M. Stein, Elissa A. Fink, Trent E. Balius, Jens Carlsson, John J. Irwin & Brian K. Shoichet
Nature Protocols, December 2021
An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
Jin Li, Ailing Fu, Le Zhang
Interdisciplinary Sciences: Computational Life Sciences, March 2019
Progress in molecular docking
Jiyu Fan, Ailing Fu, Le Zhang
Quantitative Biology, June 2019
Molecular Docking: Shifting Paradigms in Drug Discovery
Luca Pinzi, Giulio Rastelli
International Journal of Molecular Sciences, September 2019
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Chao Shen, Junjie Ding, Zhe Wang, Dongsheng Cao, Xiaoqin Ding, Tingjun Hou
WIREs computational molecular science, June 2019
Software for molecular docking: a review
Nataraj S. Pagadala, Khajamohiddin Syed, Jack Tuszynski
Biophysical Reviews, January 2017
Dynamic Docking: A Paradigm Shift in Computational Drug Discovery
Gioia, Dario, Martina Bertazzo, Maurizio Recanatini, Matteo Masetti, Andrea Cavalli
Molecules, November 2017
Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods
Xing Du, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji, Shu-Qun Liu
International Journal of Molecular Sciences, January 2016
Protein–Protein Docking: Past, Present, and Future
Sharon Sunny, PB Jayaraj
The protein journal, February 2022
A survey on computational models for predicting protein–protein interactions
Lun Hu, Xiaojuan Wang, Yu-An Huang, Pengwei Hu, Zhu-Hong You
Briefings in Bioinformatics, September 2021
What method to use for protein–protein docking?
Kathryn Porter, Israel Desta, Dima Kozakov, Sandor Vajda
Current Opinion in Structural Biology, April 2019
Challenges in structural modeling of RNA-protein interactions
Xudong Liu, Yingtian Duan, Xu Hong, Juan Xie, Shiyong Liu
Current Opinion in Structural Biology, June 2023
Protein–RNA interaction prediction with deep learning: structure matters
Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li
Briefings in Bioinformatics， January 2022
Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices
Abdul-Quddus Kehinde Oyedele, Abdeen Tunde Ogunlana, Ibrahim Damilare Boyenle, Ayodeji Oluwadamilare Adeyemi, Temionu Oluwakemi Rita, Temitope Isaac Adelusi, Misbaudeen Abdul-Hammed, Oluwabamise Emmanuel Elegbeleye, Tope Tunji Odunitan
Molecular Diversity, September 2022
- Smina
- 🔥AutoDock Suite
- AutoDock-GPU
- MGLTools software suite
- SwissDock
- rDock
- GOLD Suite
- GLIDE
- MOE-Dock