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In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery.
Distributed automated docking of flexible ligands to proteins: parallel applications of autodock 2.4
Garrett M Morris, David S Goodsell, Ruth Huey, and Arthur J Olson · 1996
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The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
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Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening
Thomas A Halgren, Robert B Murphy, Richard A Friesner, Hege S Beard, Leah L Frye, W Thomas Pollard, and Jay L Banks · 2004
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Quantifying biogenic bias in screening libraries
Jérôme Hert, John J Irwin, Christian Laggner, Michael J Keiser, and Brian K Shoichet · 2009
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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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Nnscore 2.0: a neural-network receptor–ligand scoring function
Jacob D Durrant and J Andrew McCammon · 2011
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Automated minimization of steric clashes in protein structures
Srinivas Ramachandran, Pradeep Kota, Feng Ding, and Nikolay V Dokholyan · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Exploring chemical space for drug discovery using the chemical universe database
Jean-Louis Reymond and Mahendra Awale · 2012
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Ligand pose and orientational sampling in molecular docking
Ryan G Coleman, Michael Carchia, Teague Sterling, John J Irwin, and Brian K Shoichet · 2013
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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
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
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Fipsdock: a new molecular docking technique driven by fully informed swarm optimization algorithm
Yu Liu, Lei Zhao, Wentao Li, Dongyu Zhao, Miao Song, and Yongliang Yang · 2013
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Euclidean distance geometry and applications
Leo Liberti, Carlile Lavor, Nelson Maculan, and Antonio Mucherino · 2014
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Molecular docking and structure-based drug design strategies
Leonardo G Ferreira, Ricardo N Dos Santos, Glaucius Oliva, and Adriano D Andricopulo · 2015
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Better informed distance geometry: using what we know to improve conformation generation
Sereina Riniker and Gregory A Landrum · 2015
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The nextprot knowledgebase on human proteins: 2017 update
Pascale Gaudet, Pierre-André Michel, Monique Zahn-Zabal, Aurore Britan, Isabelle Cusin, Marcin Domagalski, Paula D Duek, Alain Gateau, Anne Gleizes, Valérie Hinard, et al · 2017
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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
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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
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Accurate de novo prediction of protein contact map by ultra-deep learning model
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu · 2017
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Vector neurons: A general framework for so (3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J Guibas · 2021
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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
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Gnina 1.0: molecular docking with deep learning
Andrew T McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, and David Ryan Koes · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona, and Jörg Kurt Wegner · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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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.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Uniprot: a worldwide hub of protein knowledge
UniProt Consortium · 2019
Cited alongside, same era.
Ultra-large library docking for discovering new chemotypes
Jiankun Lyu, Sheng Wang, Trent E Balius, Isha Singh, Anat Levit, Yurii S Moroz, Matthew J O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova, et al · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Paul G Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B Iovanisci, Ian Snyder, and David R Koes · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıćtor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Equivariant graph mechanics networks with constraints
Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, 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
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Deep learning model for flexible and efficient protein-ligand docking
Matthew Masters, Amr H Mahmoud, Yao Wei, and Markus Alexander Lill · 2022
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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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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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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
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Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, et al · 2022
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