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Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome.
Development and validation of a genetic algorithm for flexible docking
Gareth Jones, Peter Willett, Robert C Glen, Andrew R Leach, and Robin Taylor · 1997
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Announcing the worldwide Protein Data Bank
H. Berman, K. Henrick, and H. Nakamura · 2003
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Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine
Ajay N Jain · 2003
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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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Binding moad (mother of all databases)
Liegi Hu, Mark L Benson, Richard D Smith, Michael G Lerner, and Heather A Carlson · 2005
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson · 2006
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Moldock: a new technique for high-accuracy molecular docking
René Thomsen and Mikael H Christensen · 2006
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Bindingdb: a web-accessible database of experimentally determined protein–ligand binding affinities
Tiqing Liu, Yuhmei Lin, Xin Wen, Robert N Jorissen, and Michael K Gilson · 2007
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Protein–ligand docking with evolutionary algorithms
René Thomsen · 2007
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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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Ecod: an evolutionary classification of protein domains
Hua Cheng, R Dustin Schaeffer, Yuxing Liao, Lisa N Kinch, Jimin Pei, Shuoyong Shi, Bong-Hyun Kim, and Nick V Grishin · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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A brief survey on bio inspired optimization algorithms for molecular docking
Mayukh Mukhopadhyay · 2014
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Fast, accurate, and reliable molecular docking with QuickVina 2
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh · 2015
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Uniprot: a hub for protein information
UniProt Consortium · 2015
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Ligands binding and molecular simulation: the potential investigation of a biosensor based on an insect odorant binding protein
Xin Yi, Yanbo Zhang, Peidan Wang, Jiangwei Qi, Meiying Hu, and Guohua Zhong · 2015
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Computational protein–ligand docking and virtual drug screening with the autodock suite
Stefano Forli, Ruth Huey, Michael E Pique, Michel F Sanner, David S Goodsell, and Arthur J Olson · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
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
Cited alongside, same era.
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.
Molecular docking for prediction and interpretation of adverse drug reactions
Heng Luo, Achille Fokoue-Nkoutche, Nalini Singh, Lun Yang, Jianying Hu, and Ping Zhang · 2018
Cited alongside, same era.
Rl-mlzerd: Multimeric protein docking using reinforcement learning
Tunde Aderinwale, Charles Christoffer, and Daisuke Kihara · 2022
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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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Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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Riemannian score-based generative modeling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
An open-source drug discovery platform enables ultra-large virtual screens
Christoph Gorgulla, Andras Boeszoermenyi, Zi-Fu Wang, Patrick D Fischer, Paul W Coote, Krishna M Padmanabha Das, Yehor S Malets, Dmytro S Radchenko, Yurii S Moroz, David A Scott, et al · 2020
Cited alongside, same era.
spyrmsd: symmetry-corrected rmsd calculations in python
Rocco Meli and Philip C. Biggin · 2020
Cited alongside, same era.
A defined structural unit enables de novo design of small-molecule–binding proteins
Nicholas F Polizzi and William F DeGrado · 2020
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
Cited alongside, same era.
Iterative refinement graph neural network for antibody sequence-structure co-design
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola · 2021
Cited alongside, same era.
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, and Alexander Rives · 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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From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale
Gustavo Schottlender, Juan Manuel Prieto, Miranda Clara Palumbo, Florencia A Castello, Federico Serral, Ezequiel J Sosa, Adrián G Turjanski, Marcelo A Martì, and Darío Fernández Do Porto · 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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A reinforcement learning approach for protein–ligand binding pose prediction
Chenran Wang, Yang Chen, Yuan Zhang, Keqiao Li, Menghan Lin, Feng Pan, Wei Wu, and Jinfeng Zhang · 2022
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Modeling molecular structures with intrinsic diffusion models
Gabriele Corso · 2023
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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, et al · 2023
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Fusiondock: Physics-informed diffusion model for molecular docking
Matthew R Masters, Amr H Mahmoud, and Markus A Lill · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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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 · 2023
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Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane · 2024
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