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Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design.
Fpocket: an open source platform for ligand pocket detection
Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery · 2009
Earlier work this paper cites.
Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott and Arthur J Olson · 2010
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho · 2013
Earlier work this paper cites.
Fast, accurate, and reliable molecular docking with quickvina 2
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh · 2015
Earlier work this paper cites.
Autodockfr: advances in protein-ligand docking with explicitly specified binding site flexibility
Pradeep Anand Ravindranath, Stefano Forli, David S Goodsell, Arthur J Olson, and Michel F Sanner · 2015
Earlier work this paper cites.
Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening
Rodrigo Quiroga and Marcos A Villarreal · 2016
Earlier work this paper cites.
3d u-net: learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
Earlier work this paper cites.
Protein-ligand blind docking using quickvina-w with inter-process spatio-temporal integration
Nafisa M Hassan, Amr A Alhossary, Yuguang Mu, and Chee-Keong Kwoh · 2017
Earlier work this paper cites.
Forging the basis for developing protein–ligand interaction scoring functions
Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang · 2017
Cited alongside, same era.
P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Radoslav Krivák and David Hoksza · 2018
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
Cited alongside, same era.
Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021
Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
Later among the works it cites.
Equibind: Geometric deep learning for drug binding structure prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
Later among the works it cites.
Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2022
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Pointsite: A point cloud segmentation tool for identification of protein ligand binding atoms
Xu Yan, Yingfeng Lu, Zhen Li, Qing Wei, Xin Gao, Sheng Wang, Song Wu, and Shuguang Cui · 2022
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Cited alongside, same era.
Accelerating autodock4 with gpus and gradient-based local search
Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas F Tillack, Michel F Sanner, Andreas Koch, and Stefano Forli · 2021
Cited alongside, same era.
Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2022
Cited alongside, same era.
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
Cited alongside, same era.
Later among the works it cites.
Uni-dock: A gpu-accelerated docking program enables ultra-large virtual screening
Yuejiang Yu, Chun Cai, Zhengdan Zhu, and Hang Zheng · 2022
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E3bind: An end-to-end equivariant network for protein-ligand docking
Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong, and Jian Tang · 2022
Later among the works it cites.