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Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
David Weininger · 1988
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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The art and practice of structure-based drug design: a molecular modeling perspective
Regine S Bohacek, Colin McMartin, and Wayne C Guida · 1996
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Fast and space efficient string kernels using suffix arrays
Choon Hui Teo and S.V.N. Vishwanathan · 2006
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Variational learning of inducing variables in sparse Gaussian processes
Michalis Titsias · 2009
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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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Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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ZINC 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Taking the human out of the loop: A review of Bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
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Distributed Gaussian processes
Marc Deisenroth and Jun Wei Ng · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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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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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC)
Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L Guimaraes, and Alan Aspuru-Guzik · 2017
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Chemical graph transformation with stereo-information
Jakob Lykke Andersen, Christoph Flamm, Daniel Merkle, and Peter F Stadler · 2017
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Improving screening efficiency through iterative screening using docking and conformal prediction
Fredrik Svensson, Ulf Norinder, and Andreas Bender · 2017
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Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
José Miguel Hernández-Lobato, James Requeima, Edward O Pyzer-Knapp, and Alán Aspuru-Guzik · 2017
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Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 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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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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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Efficient iterative virtual screening with Apache Spark and conformal prediction
Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure, and Ola Spjuth · 2018
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Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, and Koji Tsuda · 2018
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Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 2018
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Aditya R Thawani, Ryan-Rhys Griffiths, Arian Jamasb, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A Aldrick, and Alpha A Lee · 2020
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One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
Alice Capecchi, Daniel Probst, and Jean-Louis Reymond · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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MARS: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Accelerating high-throughput virtual screening through molecular pool-based active learning
David E Graff, Eugene I Shakhnovich, and Connor W Coley · 2021
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GuacaMol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 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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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 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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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
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Barking up the right tree: an approach to search over molecule synthesis dags
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2020
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Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio · 2021
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Therapeutics data commons: Machine learning datasets and tasks for therapeutics
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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A fresh look at de novo molecular design benchmarks
Austin Tripp, Gregor NC Simm, and José Miguel Hernández-Lobato · 2021
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
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High-dimensional Bayesian optimisation with variational autoencoders and deep metric learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, et al · 2021
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Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
AkshatKumar Nigam, Robert Pollice, Mario Krenn, Gabriel dos Passos Gomes, and Alan Aspuru-Guzik · 2021
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MIMOSA: Multi-constraint molecule sampling for molecule optimization
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, and Jimeng Sun · 2021
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Hit and lead discovery with explorative RL and fragment-based molecule generation
Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, and Sung Ju Hwang · 2021
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Deep molecular dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
Cynthia Shen, Mario Krenn, Sagi Eppel, and Alan Aspuru-Guzik · 2021
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DOCKSTRING: Easy molecular docking yields better benchmarks for ligand design
Miguel García-Ortegón, Gregor NC Simm, Austin J Tripp, José Miguel Hernández-Lobato, Andreas Bender, and Sergio Bacallado · 2021
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Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking
Francesco Gentile, Jean Charle Yaacoub, James Gleave, Michael Fernandez, Anh-Tien Ton, Fuqiang Ban, Abraham Stern, and Artem Cherkasov · 2022
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
Wenhao Gao, Rocío Mercado, and Connor W Coley · 2022
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Differentiable scaffolding tree for molecular optimization
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W Coley, and Jimeng Sun · 2022
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Local latent space bayesian optimization over structured inputs
Natalie Maus, Haydn T Jones, Juston S Moore, Matt J Kusner, John Bradshaw, and Jacob R Gardner · 2022
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Defining levels of automated chemical design
Brian Goldman, Steven Kearnes, Trevor Kramer, Patrick Riley, and W Patrick Walters · 2022
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Autonomous platforms for data-driven organic synthesis
Wenhao Gao, Priyanka Raghavan, and Connor W Coley · 2022
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Learning 3D representations of molecular chirality with invariance to bond rotations
Keir Adams, Lagnajit Pattanaik, and Connor W Coley · 2022
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Self-focusing virtual screening with active design space pruning
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An evaluation framework for the objective functions of de novo drug design benchmarks
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