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We study a fundamental problem in structure-based drug design -- generating molecules that bind to specific protein binding sites.
Elementary mathematical theory of classification and prediction
Taffee T Tanimoto · 1958
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Anthony K Rappé, Carla J Casewit, KS Colwell, William A Goddard III, and W Mason Skiff · 1992
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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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Symmetry-aware actor-critic for 3d molecular design
Gregor NC Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 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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Estimation of the size of drug-like chemical space based on gdb-17 data
Pavel G Polishchuk, Timur I Madzhidov, and Alexandre Varnek · 2013
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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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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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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas WA Gebauer, Michael Gastegger, and Kristof T Schütt · 2019
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Shape-based generative modeling for de novo drug design
Miha Skalic, José Jiménez, Davide Sabbadin, and Gianni De Fabritiis · 2019
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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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Kristof T Schütt, PJ Kindermans, Huziel E Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus R Müller · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 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 L Gaunt · 2018
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Reinforcement learning for molecular design guided by quantum mechanics
Gregor Simm, Robert Pinsler, and José Miguel Hernández-Lobato
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Deep generative models for 3d linker design
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, and Charlotte M Deane · 2020
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Composing molecules with multiple property constraints
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Tomohide Masuda, Matthew Ragoza, and David Ryan Koes · 2020
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Learning a continuous representation of 3d molecular structures with deep generative models
Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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Iterative refinement graph neural network for antibody sequence-structure co-design
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola · 2021
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