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Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}.
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Taffee T Tanimoto · 1958
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David Weininger · 1988
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Divergence measures based on the shannon entropy
Jianhua Lin · 1991
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Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
Thomas A Halgren · 1996
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The process of structure-based drug design
Amy C Anderson · 2003
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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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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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Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Dávid Bajusz, Anita Rácz, and Károly Héberger · 2015
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Pdb-wide collection of binding data: current status of the pdbbind database
Zhihai Liu, Yan Li, Li Han, Jie Li, Jie Liu, Zhixiong Zhao, Wei Nie, Yuchen Liu, and Renxiao Wang · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Molecular generation with recurrent neural networks (rnns)
Esben Jannik Bjerrum and Richard Threlfall · 2017
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Conformation generation: the state of the art
Paul CD Hawkins · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 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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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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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
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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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
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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, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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A structure-based drug discovery paradigm
Maria Batool, Bilal Ahmad, and Sangdun Choi · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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Transformercpi: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng · 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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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Structure-based de novo drug design using 3d deep generative models
Yibo Li, Jianfeng Pei, and Luhua Lai · 2021
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A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
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De novo molecule design through the molecular generative model conditioned by 3d information of protein binding sites
Mingyuan Xu, Ting Ran, and Hongming Chen · 2021
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Energy-inspired molecular conformation optimization
Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, and Jian Peng · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Generating 3d molecules for target protein binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
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Generating 3D molecules conditional on receptor binding sites with deep generative models
Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 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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Target-aware molecular graph generation
Cheng Tan, Zhangyang Gao, and Stan Z Li · 2022
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Structure-aware multimodal deep learning for drug–protein interaction prediction
Penglei Wang, Shuangjia Zheng, Yize Jiang, Chengtao Li, Junhong Liu, Chang Wen, Atanas Patronov, Dahong Qian, Hongming Chen, and Yuedong Yang · 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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