🧬 Awesome Docking

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:

If you have a paper or resource you'd like to add, please submit a pull request or open an issue.

Versatile Docking

🔥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
Preprint, October 2023

Survey

Protein-Ligand

🔥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 Discoveryg
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

Protein-Nucleic Acid

Protein-Ligand Docking

2023 -- Protein-Ligand

Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Jiaxian Yan, Zaixi Zhang, Kai Zhang, Qi Liu Preprint, December 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

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

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

2022 -- Protein-Ligand

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

Protein-Protein Docking

2023 -- Protein-Protein

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

2021 -- Protein-Protein

🔥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

Protein-Nucleic Acid Docking

2023 -- Protein-Nucleic Acid

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

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

2022 -- Protein-Nucleic Acid

Protein–RNA interaction prediction with deep learning: structure matters
Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li
Briefings in Bioinformatics， January 2022

Covalent Docking

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices
A. Oyedele, A. Ogunlana, I. Boyenle, A. Adeyemi, Temionu Oluwakemi Rita, Temitope Isaac Adelusi, M. Abdul-Hammed, Oluwabamise Emmanuel Elegbeleye, Tope T. Odunitan
Molecular Diversity, September 2022

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, Aurélien F. A. Moumbock, Ammar Qaseem, Qianqing Xu, S. Günther
Nucleic Acids Research, September 2021

Fragment-based covalent ligand discovery
Wenchao Lu, M. Kostic, Tinghu Zhang, Jianwei Che, M. Patricelli, L. Jones, Edward T. Chouchani, N. Gray
RSC Chemical Biology, February 2021

Covalent docking of large libraries for the discovery of chemical probes
N. London, Randy M. Miller, Shyam Krishnan, K. Uchida, J. Irwin, O. Eidam, L. Gibold, Peter Cimermancic, R. Bonnet, B. Shoichet, J. 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, C. Su, Z. Ge, Runtao Li, C. Kwoh
Journal of Computational Chemistry, February 2013