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Structure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challenge in drug discovery.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Autodock4 and autodocktools4: Automated docking with selective receptor flexibility
Garrett M Morris, Ruth Huey, William Lindstrom, Michel F Sanner, Richard K Belew, David S Goodsell, and Arthur J Olson · 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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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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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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Multi-objective reinforcement learning: A comprehensive overview
Chunming Liu, Xin Xu, and Dewen Hu · 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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Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening
Rodrigo Quiroga and Marcos A Villarreal · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 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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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Structure-based drug design: aiming for a perfect fit
Rob LM Van Montfort and Paul Workman · 2017
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Can ai reproduce observed chemical diversity?
Mostapha Benhenda · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
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Comparative assessment of scoring functions: the casf-2016 update
Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, and Renxiao Wang · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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Randomized smiles strings improve the quality of molecular generative models
Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Dive into deep learning: Tools for engagement
Joanne Quinn, Joanne McEachen, Michael Fullan, Mag Gardner, and Max Drummy · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Guiding deep molecular optimization with genetic exploration
Sungsoo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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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
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and T. Jaakkola · 2020
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Reinforcement learning for molecular design guided by quantum mechanics
Gregor Simm, Robert Pinsler, and José Miguel Hernández-Lobato · 2020
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Autogrow4: an open-source genetic algorithm for de novo drug design and lead optimization
Jacob O Spiegel and Jacob D Durrant · 2020
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A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
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Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, and Gisbert Schneider · 2021
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al · 2021
Cited alongside, same era.
Molgpt: molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
Cited alongside, same era.
Unified 2d and 3d pre-training of molecular representations
Jinhua Zhu, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu · 2022
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The role of ai in drug discovery: challenges, opportunities, and strategies
Alexandre Blanco-Gonzalez, Alfonso Cabezon, Alejandro Seco-Gonzalez, Daniel Conde-Torres, Paula Antelo-Riveiro, Angel Pineiro, and Rebeca Garcia-Fandino · 2023
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Structure-aware protein self-supervised learning
Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou · 2023
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2023
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Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
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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
Cited alongside, same era.
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu, et al · 2021
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.
A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Property-aware relation networks for few-shot molecular property prediction
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, and Dejing Dou · 2021
Cited alongside, same era.
Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
Cited alongside, same era.
xval: A continuous number encoding for large language models
Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, et al · 2023
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Benchmarking generated poses: How rational is structure-based drug design with generative models?
Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell · 2023
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De novo drug design using reinforcement learning with multiple gpt agents
Xiuyuan Hu, Guoqing Liu, Yang Zhao, and Hao Zhang · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
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Artificial intelligence in drug discovery and development
Kit-Kay Mak, Yi-Hang Wong, and Mallikarjuna Rao Pichika · 2023
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Utilizing reinforcement learning for de novo drug design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, and Morteza Haghir Chehreghani · 2023
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A systematic survey of chemical pre-trained models
Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, and Stan Z Li · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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Uni-mol docking v2: Towards realistic and accurate binding pose prediction
Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, and Gengmo Zhou · 2024
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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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Deep confident steps to new pockets: Strategies for docking generalization
Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola · 2024
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Chai-1: Decoding the molecular interactions of life
Chai Discovery, Jacques Boitreaud, Jack Dent, Matthew McPartlon, Joshua Meier, Vinicius Reis, Alex Rogozhnikov, and Kevin Wu · 2024
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Machine learning-aided generative molecular design
Yuanqi Du, Arian R Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, and Tom L Blundell · 2024
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Hamiltonian diversity: effectively measuring molecular diversity by shortest hamiltonian circuits
Xiuyuan Hu, Guoqing Liu, Quanming Yao, Yang Zhao, and Hao Zhang · 2024
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Drugpose: benchmarking 3d generative methods for early stage drug discovery
Zygimantas Jocys, Joanna Grundy, and Katayoun Farrahi · 2024
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Drug discovery with dynamic goal-aware fragments
Seul Lee, Seanie Lee, Kenji Kawaguchi, and Sung Ju Hwang · 2024
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Deep learning for protein-ligand docking: Are we there yet?
Alex Morehead, Nabin Giri, Jian Liu, and Jianlin Cheng · 2024
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Caught between a rock and a hard place: current challenges in structure-based drug design
Daniele Pala and David E Clark · 2024
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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, and Rui Yan · 2024
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Structure-based drug design benchmark: Do 3d methods really dominate?
Kangyu Zheng, Yingzhou Lu, Zaixi Zhang, Zhongwei Wan, Yao Ma, Marinka Zitnik, and Tianfan Fu · 2024
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Artem Zholus, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Daniil Polykovskiy, Sarath Chandar, and Alex Zhavoronkov · 2024
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