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Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery.
Accelerating large-scale inference with anisotropic vector quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar · 1908
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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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Virtual screening—an overview
W.Patrick Walters, Matthew T Stahl, and Mark A Murcko · 1998
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ZINC - a free database of commercially available compounds for virtual screening
John J. Irwin and Brian K. Shoichet · 2004
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BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
T. Liu, Y. Lin, X. Wen, R. N. Jorissen, and M. K. Gilson · 2007
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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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Advances and applications of binding affinity prediction methods in drug discovery
Marco Daniele Parenti and Giulio Rastelli · 2011
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Improving drug candidates by design: A focus on physicochemical properties as a means of improving compound disposition and safety
Nicholas A. Meanwell · 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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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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Computational methods in drug discovery
Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, and Edward W. Lowe · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 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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The ChEMBL database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J. Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach · 2016
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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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Mur ligase inhibitors as anti-bacterials: A comprehensive review
Jaiprakash N. Sangshetti, Suyog S. Joshi, Rajendra H. Patil, Mark G. Moloney, and Devanand B. Shinde · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2017
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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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Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, and Woo Youn Kim · 2018
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Advances and challenges in deep generative models for de novo molecule generation
Dongyu Xue, Yukang Gong, Zhaoyi Yang, Guohui Chuai, Sheng Qu, Aizong Shen, Jing Yu, and Qi Liu · 2018
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Deep learning for molecular generation
Youjun Xu, Kangjie Lin, Shiwei Wang, Lei Wang, Chenjing Cai, Chen Song, Luhua Lai, and Jianfeng Pei · 2018
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mmpdb: An open-source matched molecular pair platform for large multiproperty data sets
Andrew Dalke, Jérôme Hert, and Christian Kramer · 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, Rex Ying, Vijay Pande, and Jure Leskovec · 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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Retrieval-based neural code generation
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig · 2018
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Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, and Shanrong Zhao · 2019
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Artificial intelligence in drug discovery: recent advances and future perspectives
José Jiménez-Luna, Francesca Grisoni, Nils Weskamp, and Gisbert Schneider · 2021
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Optimizing molecules using efficient queries from property evaluations
Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das · 2021
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Generative AI models for drug discovery
Bowen Tang, John Ewalt, and Ho-Leung Ng · 2021
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Can generative-model-based drug design become a new normal in drug discovery?
Hongming Chen · 2021
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Controlled molecule generator for optimizing multiple chemical properties
Bonggun Shin, Sungsoo Park, JinYeong Bak, and Joyce C. Ho · 2021
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
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Jan H. Jensen · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 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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Learning multimodal graph-to-graph translation for molecule optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola · 2019
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Hierarchical Graph-to-Graph Translation for Molecules
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 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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Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn, and Alán Aspuru-Guzik · 2019
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Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy P. K. Tan, James Hedrick, Jason Crain, and Aleksandra Mojsilovic · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud et al · 2021
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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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Molecule optimization by explainable evolution
Binghong Chen*, Tianzhe Wang*, Chengtao Li, Hanjun Dai, and Le Song · 2021
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Accelerating AutoDock VINA with GPUs
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Potent noncovalent inhibitors of the main protease of SARS-CoV-2 from molecular sculpting of the drug perampanel guided by free energy perturbation calculations
Chun-Hui Zhang, Elizabeth A. Stone, Maya Deshmukh, Joseph A. Ippolito, Mohammad M. Ghahremanpour, Julian Tirado-Rives, Krasimir A. Spasov, Shuo Zhang, Yuka Takeo, Shalley N. Kudalkar, Zhuobin Liang, Farren Isaacs, Brett Lindenbach, Scott J. Miller, Karen S. Anderson, and William L. Jorgensen · 2021
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Instance-conditioned gan
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Msa transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper et al · 2021
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Chemformer: a pre-trained transformer for computational chemistry
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum · 2022
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
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Learning to extend molecular scaffolds with structural motifs
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Relational Memory Augmented Language Models
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Memorizing transformers
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GreaseLM: Graph REASoning enhanced language models
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Re-imagen: Retrieval-augmented text-to-image generator
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Drug discovery and development: technology in transition
Ray G Hill and Duncan Richards · 2022
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