https://github.com/KyGao/awesome-docking
An awesome & curated list of docking papers
https://github.com/KyGao/awesome-docking
List: awesome-docking
awesome awesome-list docking
Last synced: 4 months ago
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An awesome & curated list of docking papers
- Host: GitHub
- URL: https://github.com/KyGao/awesome-docking
- Owner: KyGao
- License: gpl-3.0
- Created: 2023-11-23T06:39:12.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-12T14:10:04.000Z (4 months ago)
- Last Synced: 2025-01-12T15:24:34.006Z (4 months ago)
- Topics: awesome, awesome-list, docking
- Homepage:
- Size: 1.98 MB
- Stars: 89
- Watchers: 8
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-docking - An awesome & curated list of docking papers. (Other Lists / Julia Lists)
README
𧏠Awesome Docking
[](https://awesome.re)  
**AlphaFold-latest**đ„ (with newly updated **AlphaFold3**đ„ )and **RFAA**đ„ 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.
## Categories
- [Versatile Docking](#versatile-docking)
- [Protein-Ligand Docking](#protein-ligand-docking)
- [2024 -- Protein-Ligand](#2024----protein-ligand)
- [2023 -- Protein-Ligand](#2023----protein-ligand)
- [2022 -- Protein-Ligand](#2022----protein-ligand)
- [Protein-Protein Docking](#protein-protein-docking)
- [2024 -- Protein-Protein](#2024----protein-protein)
- [2023 -- Protein-Protein](#2023----protein-protein)
- [2022 -- Protein-Protein](#2022----protein-protein)
- [2021 -- Protein-Protein](#2021----protein-protein)
- [Protein-Nucleic Acid Docking](#protein-nucleic-acid-docking)
- [2023 -- Protein-Nucleic Acid](#2023----protein-nucleic-acid)
- [Covalent Docking](#covalent-docking)
- [Survey](#survey)
- [Protein-Ligand](#protein-ligand)
- [Protein-Protein](#protein-protein)
- [Protein-Nucleic Acid](#protein-nucleic-acid)
- [Covalent](#covalent)
- [Traditional Docking Tools](#traditional-docking-tools)
- [Open-source and free access](#open-source-and-free-access)
- [Commercial tools](#commercial-tool)
---
## Versatile Docking
**Boltz-1: DemocratizingBiomolecularInteractionModeling**
Jeremy Wohlwend*, Gabriele Corso*, Saro Passaro*, Mateo Reveiz, Ken Leida, Wojtek Swiderski, Tally Portnoi, Itamar Chinn, Jacob Silterra, Tommi Jaakkola, Regina Barzilay
*Report, Nov 2024*
[](https://gcorso.github.io/assets/boltz1.pdf)
[](https://github.com/jwohlwend/boltz)
**Chai-1 Technical Report**
Chai-1 Discovery Team
*Report, Sep 2024*
[](https://chaiassets.com/chai-1/paper/technical_report_v1.pdf)
[](https://github.com/chaidiscovery/chai-lab/)
**AlphaFold3 Open-Source Implementation (Ligo)**
Edward Harris, Emily Egerton-Warburton, Arda Goreci
*Project, Sep 2024*
[](https://github.com/Ligo-Biosciences/AlphaFold3)
đ„**Accurate structure prediction of biomolecular interactions with AlphaFoldâ3**
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J. Ballard, Joshua Bambrick, Sebastian W. Bodenstein, David A. Evans, Chia-Chun Hung, Michael OâNeill, David Reiman, Kathryn Tunyasuvunakool, Zachary Wu, AkvilÄ ĆœemgulytÄ, Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexey Cherepanov, Miles Congreve, Alexander I. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B. Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Caroline M. R. Low, Kuba Perlin, Anna Potapenko, Pascal Savy, Sukhdeep Singh, Adrian Stecula, Ashok Thillaisundaram, Catherine Tong, Sergei Yakneen, Ellen D. Zhong, Michal Zielinski, Augustin ĆœĂdek, Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis & John M. Jumper
*Nature, May 2024*
[](https://www.nature.com/articles/s41586-024-07487-w)
[](https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/)
[](https://golgi.sandbox.google.com/)
đ„**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*
[](https://www.science.org/doi/10.1126/science.adl2528)
[](https://github.com/baker-laboratory/RoseTTAFold-All-Atom)
đ„**A glimpse of the next generation of AlphaFold**
Google DeepMind AlphaFold team and Isomorphic Labs team
*News, Oct 2023*
[](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/a-glimpse-of-the-next-generation-of-alphafold/alphafold_latest_oct2023.pdf)
[](https://deepmind.google/discover/blog/a-glimpse-of-the-next-generation-of-alphafold)
**The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions**
Zymvol Biomodeling
*Nucleic Acids Research, July 2023*
[](https://academic.oup.com/nar/article/51/W1/W298/7151343?login=false)
[](https://github.com/lightdock/lightdock)
## Protein-Ligand Docking
### 2024 -- Protein-Ligand
**SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate ProteinâLigand Complex Prediction**
Duanhua Cao, Mingan Chen, Runze Zhang, Zhaokun Wang, Manlin Huang, Jie Yu, Xinyu Jiang, Zhehuan Fan, Wei Zhang, Hao Zhou, Xutong Li, Zunyun Fu, Sulin Zhang, Mingyue Zheng
*Nature Methods, Nov 2024*
[](https://www.nature.com/articles/s41592-024-02516-y)
[](https://github.com/CAODH/SurfDock)
**QuickBind: A Light-Weight And Interpretable Molecular Docking Model**
Wojtek Treyde, Seohyun Chris Kim, Nazim Bouatta, Mohammed AlQuraishi
*MLCB, Oct 2024*
[](https://proceedings.mlr.press/v261/treyde24a.html)
[](https://github.com/aqlaboratory/QuickBind)
**One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning**
Kelei He, Tiejun Dong, Jinhui Wu, Junfeng Zhang
*Preprint, Aug 2024*
[](https://arxiv.org/pdf/2408.11356)
[](https://gitfront.io/r/LigPose/kMWuV4DW6JpE/LigPose4Review/)
**Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modelling**
Zhihao Wang, Fan Zhou, Zechen Wang, Qiuyue Hu, Yong-Qiang Li, Sheng Wang, Yanjie Wei, Liangzhen Zheng, Weifeng Li, Xiangda Peng
*JCIM, July 2024*
[](https://pubs.acs.org/doi/10.1021/acs.jcim.4c00669)
**Deep Learning for Protein-Ligand Docking: Are We There Yet?**
Alex Morehead, Nabin Giri, Jian Liu, Jianlin Cheng
*Preprint, May 2024*
[](https://arxiv.org/pdf/2405.14108)
[](https://github.com/BioinfoMachineLearning/PoseBench)
**Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction**
Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, Gengmo Zhou
*Preprint, May 2024*
[](https://arxiv.org/pdf/2405.11769)
[](https://github.com/dptech-corp/Uni-Mol)
**CarsiDock: a deep learning paradigm for accurate proteinâligand 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*
[](https://pubs.rsc.org/en/content/articlepdf/2024/sc/d3sc05552c)
[](https://github.com/carbonsilicon-ai/CarsiDock)
**GeoDirDock: Guiding Docking Along Geodesic Paths**
RaĂșl Miñån, Javier Gallardo, Ălvaro Ciudad, Alexis Molina
*Preprint, April 2024*
[](https://arxiv.org/pdf/2404.06481.pdf)
**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*
[](https://www.researchsquare.com/article/rs-3995849/v1)
**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*
[](https://arxiv.org/pdf/2403.20261.pdf)
[](https://github.com/QizhiPei/FABind)
**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*
[](https://www.biorxiv.org/content/10.1101/2024.03.14.585019)
[](https://www.biopharmatrend.com/post/713-artidock-from-receptorai-next-generation-ai-docking-that-beats-diffdock-and-alphafold-latest)
**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*
[](https://arxiv.org/pdf/2402.11459.pdf)
**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*
[](https://www.biorxiv.org/content/10.1101/2024.01.31.578200v1.full.pdf)
[](https://github.com/Zhang-Runze/PackDock)
**State-specific proteinâligand 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*
[](https://www.nature.com/articles/s42256-024-00792-z)
[](https://github.com/zrqiao/NeuralPLexer)
**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*
[](https://www.nature.com/articles/s41467-024-45461-2)
[](https://github.com/luwei0917/DynamicBind)
**Deep confident steps to new pockets: strategies for docking generalization**
Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola
*ICLR, Feburary 2024*
[](https://openreview.net/forum?id=UfBIxpTK10)
[](https://github.com/LDeng0205/confidence-bootstrapping)
**(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*
[](https://arxiv.org/pdf/2401.15122.pdf)
### 2023 -- Protein-Ligand
**(DeltaDock) Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking**
Jiaxian Yan, Zaixi Zhang, Kai Zhang, Qi Liu
*Preprint, December 2023*
[](https://arxiv.org/abs/2311.18574)
**DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking**
Jintao Zhu, Zhonghui Gu, Jianfeng Pei, Luhua Lai
*Preprint, November 2023*
[](https://arxiv.org/pdf/2311.15201.pdf)
[](https://github.com/HBioquant/DiffBindFR)
**Structure prediction of protein-ligand complexes from sequence information with Umol**
Patrick Bryant, Atharva Kelkar, Andrea Guljas, Cecilia Clementi, Frank Noé
*Preprint, November 2023*
[](https://pubmed.ncbi.nlm.nih.gov/37745556/)
[](https://github.com/patrickbryant1/Umol)
**(FlexPose) Equivariant Flexible Modeling of the ProteinâLigand Binding Pose with Geometric Deep Learning**
Tiejun Dong, Ziduo Yang, Jun Zhou, and Calvin Yu-Chian Chen
*JCTC, November 2023*
[](https://pubs.acs.org/doi/full/10.1021/acs.jctc.3c00273)
[](https://github.com/tiejundong/FlexPose)
**Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models**
Lihang Liu, Shanzhuo Zhang, Donglong He, Xianbin Ye, Jingbo Zhou, Xiaonan Zhang, Yaoyao Jiang, Weiming Diao, Hang Yin, Hua Chai, Fan Wang, Jingzhou He, Liang Zheng, Yonghui Li, Xiaomin Fang
*Preprint, October 2023*
[](https://arxiv.org/abs/2310.13913)
[](https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/molecular_docking/helixdock)
**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.*
[](https://arxiv.org/abs/2308.05777)
[](https://github.com/maabuu/posebusters)
**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*
[](https://openreview.net/forum?id=PnWakgg1RL)
[](https://github.com/QizhiPei/FABind)
**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*
[](https://doi.org/10.1038/s43588-023-00511-5)
[](https://github.com/schrojunzhang/KarmaDock)
**(EDM-Dock) Deep Learning Model for Efficient ProteinâLigand Docking with Implicit Side-Chain Flexibility**
Matthew R. Masters, Amr H. Mahmoud, Yao Wei, and Markus A. Lill
*JCIM, March 2023*
[](https://doi.org/10.1038/s43588-023-00511-5)
[](https://github.com/MatthewMasters/EDM-Dock)
**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*
[](https://openreview.net/forum?id=JrtHZdbGtN)
đ„**DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking**
Gabriele Corso, Hannes StÀrk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
*ICLR, Feburary 2023*
[](https://openreview.net/forum?id=kKF8_K-mBbS)
[](https://github.com/gcorso/DiffDock)
**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*
[](https://openreview.net/forum?id=6K2RM6wVqKu)
[](https://github.com/dptech-corp/Uni-Mol)
**E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking**
Yangtian Zhang, Huiyu Cai, Chence Shi, Jian Tang
*ICLR, Feburary 2023*
[](https://openreview.net/forum?id=sO1QiAftQFv)
### 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*
[](https://openreview.net/forum?id=MSBDFwGYwwt)
[](https://github.com/luwei0917/TankBind)
**EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction**
Hannes StÀrk, Octavian Ganea, Lagnajit Pattanaik, Dr.Regina Barzilay, Tommi Jaakkola
*ICML, July 2022*
[](https://proceedings.mlr.press/v162/stark22b.html)
[](https://github.com/HannesStark/EquiBind)
## Protein-Protein Docking
### 2024 -- Protein-Protein
**Deep Reinforcement Learning for Modelling Protein Complexes**
Ziqi Gao, Tao Feng, Jiaxuan You, Chenyi Zi, Yan Zhou, Chen Zhang, Jia Li
*ICLR, March 2024*
[](https://arxiv.org/pdf/2405.02299)
**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*
[](https://www.nature.com/articles/s41592-023-02130-4)
[](https://zhanggroup.org/DeepMSA)
[](https://zhanggroup.org/DMFold)
**Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model**
Huaijin Wu, Wei Liu, Yatao Bian, Jiaxiang Wu, Nianzu Yang, Junchi Yan
*ICLR, March 2024*
[](https://openreview.net/forum?id=qg2boc2AwU)
[](https://github.com/wuhuaijin/EBMDock)
**Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction**
Ziyang Yu, Wenbing Huang, Yang Liu
*ICLR, March 2024*
[](https://openreview.net/forum?id=zgQ0PHeGnL)
[](https://github.com/yaledeus/ElliDock)
### 2023 -- Protein-Protein
**(GeoDock) Flexible proteinâprotein docking with a multitrack iterative transformer**
Lee-Shin Chu, Jeffrey A. Ruffolo, Ameya Harmalkar, Jeffrey J. Gray
*Protein Science, December 2023*
[](https://onlinelibrary.wiley.com/doi/epdf/10.1002/pro.4862)
[](https://github.com/Graylab/GeoDock)
**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*
[](https://www.nature.com/articles/s42003-023-05525-3)
[](https://github.com/BioinfoMachineLearning/MULTICOM3)
**DockGame: Cooperative Games for Multimeric Rigid Protein Docking**
Vignesh Ram Somnath, Pier Giuseppe Sessa, Maria Rodriguez Martinez, Andreas Krause
*Preprint, October 2023*
[](https://arxiv.org/abs/2310.06177)
[](https://github.com/vsomnath/dockgame)
**Diffdock-pp: Rigid protein-protein docking with diffusion models**
Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes StÀrk, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola
*ICLR workshop MLDD, March 2023*
[](https://openreview.net/forum?id=AM7WbQxuRS)
[](https://github.com/ketatam/DiffDock-PP)
**Deep Learning for Flexible and Site-Specific Protein Docking and Design**
Matthew McPartlon, Jinbo Xu
*BioRxiv, April 2023*
[](https://www.biorxiv.org/content/10.1101/2023.04.01.535079)
### 2022 -- Protein-Protein
**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*
[](https://openreview.net/forum?id=5yn5shS6wN)
**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*
[](https://openreview.net/forum?id=GQjaI9mLet)
[](https://github.com/octavian-ganea/equidock_public)
### 2021 -- Protein-Protein
đ„**Protein complex prediction with AlphaFold-Multimer**
Richard Evans, Michael OâNeill, A. Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin ZĂdek, 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*
[](https://www.biorxiv.org/content/10.1101/2021.10.04.463034)
[](https://github.com/google-deepmind/alphafold)
## Protein-Nucleic Acid Docking
### 2023 -- Protein-Nucleic Acid
đ„**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*
[](https://www.nature.com/articles/s41592-023-02086-5)
[](https://github.com/uw-ipd/RoseTTAFold2NA)
**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*
[](https://www.biorxiv.org/search/Protein-Nucleic%252BAcid%252B)
[](https://github.com/Bhattacharya-Lab/EquiPNAS)
**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*
[](https://www.nature.com/articles/s41467-023-36720-9)
[](https://github.com/Zhaolab-GitHub/DRPScore_v1.0)
## Covalent Docking
**Cov_DOX: A Method for Structure Prediction of Covalent ProteinâLigand Bindings**
Lin Wei, Yaru Chen, Jiaqi Liu, Li Rao, Yanliang Ren, Xin Xu, Jian Wan
*Journal of Medicinal Chemistry, March 2022*
[](https://pubs.acs.org/doi/full/10.1021/acs.jmedchem.1c02007)
**CovPDB: a high-resolution coverage of the covalent proteinâligand interactome**
Mingjie Gao, Aurelien F. A. Moumbock, Ammar Qaseem, Qianqing Xu, Stefan Gunther
*Nucleic Acids Research, September 2021*
[](https://academic.oup.com/nar/article/50/D1/D445/6377397)
[](https://drug-discovery.vm.uni-freiburg.de/covpdb/)
**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*
[](https://pubs.rsc.org/en/content/articlelanding/2021/CB/D0CB00222D)
**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 CimermanÄiÄ, Richard Bonnet, Brian K Shoichet, Jack Taunton
*Nature Chemical Biology, September 2014*
[](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4232467/)
[](https://drug-discovery.vm.uni-freiburg.de/covpdb/)
**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*
[](https://pubs.acs.org/doi/10.1021/ci500118s)
**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*
[](https://onlinelibrary.wiley.com/doi/10.1002/jcc.23136)
[](https://code.google.com/archive/p/covalentdock/)
## Survey
### Protein-Ligand
**Machine-learning methods for ligandâprotein molecular docking**
Kevin Crampon, Alexis Giorkallos, Myrtille Deldossi, Stéphanie Baud, Luiz Angelo Steffenel
*Drug Discovery Today, January 2022*
[](https://www.sciencedirect.com/science/article/abs/pii/S1359644621003974)
**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*
[](https://www.nature.com/articles/s41596-021-00597-z)
**An Overview of Scoring Functions Used for ProteinâLigand Interactions in Molecular Docking**
Jin Li, Ailing Fu, Le Zhang
*Interdisciplinary Sciences: Computational Life Sciences, March 2019*
[](https://link.springer.com/article/10.1007/s12539-019-00327-w)
**Progress in molecular docking**
Jiyu Fan, Ailing Fu, Le Zhang
*Quantitative Biology, June 2019*
[](https://link.springer.com/article/10.1007/s40484-019-0172-y)
**Molecular Docking: Shifting Paradigms in Drug Discovery**
Luca Pinzi, Giulio Rastelli
*International Journal of Molecular Sciences, September 2019*
[](https://www.mdpi.com/1422-0067/20/18/4331)
**From machine learning to deep learning: Advances in scoring functions for proteinâligand docking**
Chao Shen, Junjie Ding, Zhe Wang, Dongsheng Cao, Xiaoqin Ding, Tingjun Hou
*WIREs computational molecular science, June 2019*
[](https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wcms.1429)
**Software for molecular docking: a review**
Nataraj S. Pagadala, Khajamohiddin Syed, Jack Tuszynski
*Biophysical Reviews, January 2017*
[](https://link.springer.com/article/10.1007/s12551-016-0247-1)
**Dynamic Docking: A Paradigm Shift in Computational Drug Discovery**
Gioia, Dario, Martina Bertazzo, Maurizio Recanatini, Matteo Masetti, Andrea Cavalli
*Molecules, November 2017*
[](https://www.mdpi.com/1420-3049/22/11/2029)
**Insights into ProteinâLigand 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*
[](https://www.mdpi.com/1422-0067/17/2/144)
### Protein-Protein
**ProteinâProtein Docking: Past, Present, and Future**
Sharon Sunny, PB Jayaraj
*The protein journal, February 2022*
[](https://link.springer.com/article/10.1007/s10930-021-10031-8)
**A survey on computational models for predicting proteinâprotein interactions**
Lun Hu, Xiaojuan Wang, Yu-An Huang, Pengwei Hu, Zhu-Hong You
*Briefings in Bioinformatics, September 2021*
[](https://academic.oup.com/bib/article/22/5/bbab036/6159365)
**What method to use for proteinâprotein docking?**
Kathryn Porter, Israel Desta, Dima Kozakov, Sandor Vajda
*Current Opinion in Structural Biology, April 2019*
[](https://www.sciencedirect.com/science/article/abs/pii/S0959440X18300691)
### Protein-Nucleic Acid
**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*
[](https://www.sciencedirect.com/science/article/pii/S0959440X23000970)
**ProteinâRNA interaction prediction with deep learning: structure matters**
Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li
*Briefings in BioinformaticsïŒ January 2022*
[](https://academic.oup.com/bib/article/23/1/bbab540/6470965)
### Covalent
**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*
[](https://link.springer.com/article/10.1007/s11030-022-10523-4)
## Traditional Docking Tools
### Open-source and free access
- **Smina**
[](https://sourceforge.net/projects/smina)
- đ„**AutoDock Suite**
[](https://ccsb.scripps.edu/projects/docking)
[](https://github.com/ccsb-scripps/AutoDock-Vina/releases)
- **AutoDock-GPU**
[](https://github.com/ccsb-scripps/AutoDock-GPU)
- **MGLTools software suite**
[](https://ccsb.scripps.edu/mgltools/)
- **SwissDock**
[](http://www.swissdock.ch/)
- **rDock**
[](https://rdock.github.io)
[](https://github.com/CBDD/rDock)
### Commercial tool
- **GOLD Suite**
[](https://www.ch.cam.ac.uk/computing/software/gold-suite)
- **GLIDE**
[](https://www.schrodinger.com/products/glide)
- **MOE-Dock**
[](https://www.computabio.com/molecular-docking-service.html)