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Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge.
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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Hierarchical Generation of Molecular Graphs using Structural Motifs, 2020
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2002
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Soluble epoxide hydrolase as a therapeutic target for cardiovascular diseases
John D. Imig and Bruce D. Hammock · 2009
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Novel trends in high-throughput screening
Lorenz M Mayr and Dejan Bojanic · 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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Small-molecule discovery from dna-encoded chemical libraries
Ralph E. Kleiner, Christoph E. Dumelin, and David R. Liu · 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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Discovery of 1-(1,3,5-triazin-2-yl)piperidine-4-carboxamides as inhibitors of soluble epoxide hydrolase
Reema K. Thalji, Jeff J. McAtee, Svetlana Belyanskaya, Martin Brandt, Gregory D. Brown, Melissa H. Costell, Yun Ding, Jason W. Dodson, Steve H. Eisennagel, Rusty E. Fries, Jeffrey W. Gross, Mark R. Harpel, Dennis A. Holt, David I. Israel, Larry J. Jolivette, Daniel Krosky, Hu Li, Quinn Lu, Tracy Mandichak, Theresa Roethke, Christine G. Schnackenberg, Benjamin Schwartz, Lisa M. Shewchuk, Wensheng Xie, David J. Behm, Stephen A. Douglas, Ami L. Shaw, and Joseph P. Marino · 2013
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Encoded library synthesis using chemical ligation and the discovery of seh inhibitors from a 334-million member library
Alexander Litovchick, Christoph E Dumelin, Sevan Habeshian, Diana Gikunju, Marie-Aude Guié, Paolo Centrella, Ying Zhang, Eric A Sigel, John W Cuozzo, Anthony D Keefe, et al · 2015
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Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Proximal policy optimization algorithms, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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#exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Understanding the impact of entropy on policy optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, and Dale Schuurmans · 2018
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Rllib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
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ChEMBL: towards direct deposition of bioassay data
David Mendez, Anna Gaulton, A Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F Mosquera, Prudence Mutowo, Michał Nowotka, María Gordillo-Marañón, Fiona Hunter, Laura Junco, Grace Mugumbate, Milagros Rodriguez-Lopez, Francis Atkinson, Nicolas Bosc, Chris J Radoux, Aldo Segura-Cabrera, Anne Hersey, and Andrew R Leach · 2018
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Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid x receptor modulators
D. Merk, Francesca Grisoni, Lukas Friedrich, and Gisbert Schneider · 2018
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Reaction-based enumeration, active learning, and free energy calculations to rapidly explore synthetically tractable chemical space and optimize potency of cyclin-dependent kinase 2 inhibitors
Kyle D. Konze, Pieter H. Bos, Markus K. Dahlgren, Karl Leswing, Ivan Tubert-Brohman, Andrea Bortolato, Braxton Robbason, Robert Abel, and Sathesh Bhat · 2019
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Ultra-large library docking for discovering new chemotypes
Jiankun Lyu, Sheng Wang, Trent E. Balius, Isha Singh, Anat Levit, Yurii S. Moroz, Matthew J. O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova, Andrey A. Tolmachev, Brian K. Shoichet, Bryan L. Roth, and John J. Irwin · 2019
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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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Tensor processing primitives: A programming abstraction for efficiency and portability in deep learning workloads
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, and Alexander Heinecke · 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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Distgnn: Scalable distributed training for large-scale graph neural networks
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj Kalamkar, Nesreen K Ahmed, and Sasikanth Avancha · 2021
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Trends in hit-to-lead optimization following dna-encoded library screens
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Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al · 2019
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Deep learning enables rapid identification of potent ddr1 kinase inhibitors
Alex Zhavoronkov, Yan A. Ivanenkov, Alex Aliper, Mark S. Veselov, Vladimir A. Aladinskiy, Anastasiya V. Aladinskaya, Victor A. Terentiev, Daniil A. Polykovskiy, Maksim D. Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim R. Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee, Richard Soll, David Madge, Li Xing, Tao Guo, and Alán Aspuru-Guzik · 2019
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The synthesizability of molecules proposed by generative models
Wenhao Gao and Connor W. Coley · 2020
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Deep docking: A deep learning platform for augmentation of structure based drug discovery
Francesco Gentile, Vibudh Agrawal, Michael Hsing, Anh-Tien Ton, Fuqiang Ban, Ulf Norinder, Martin E. Gleave, and Artem Cherkasov · 2020
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An open-source drug discovery platform enables ultra-large virtual screens
Christoph Gorgulla, Andras Boeszoermenyi, Zi-Fu Wang, Patrick D. Fischer, Paul W. Coote, Krishna M. Padmanabha Das, Yehor S. Malets, Dmytro S. Radchenko, Yurii S. Moroz, David A. Scott, Konstantin Fackeldey, Moritz Hoffmann, Iryna Iavniuk, Gerhard Wagner, and Haribabu Arthanari · 2020
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Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle · 2020
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High-throughput screening for the discovery of enzyme inhibitors
Matthew D. Lloyd · 2020
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Machine learning on dna-encoded libraries: A new paradigm for hit finding
Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A. Clark, John W. Cuozzo, Marie-Aude Guié, John P. Guilinger, Christelle Huguet, Christopher D. Hupp, Anthony D. Keefe, Christopher J. Mulhern, Ying Zhang, and Patrick Riley · 2020
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Christopher A. Reiher, David P. Schuman, Nicholas Simmons, and Scott E. Wolkenberg · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Efficient exploration of chemical space with docking and deep learning
Ying Yang, Kun Yao, Matthew P. Repasky, Karl Leswing, Robert Abel, Brian K. Shoichet, and Steven V. Jerome · 2021
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Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds
Maria Korshunova, Niles Huang, Stephen J. Capuzzi, Dmytro S. Radchenko, Olena V. Savych, Yuriy S. Moroz, Carrow I. Wells, Timothy Mark Willson, Alexander Tropsha, and Olexandr Isayev · 2022
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Generative deep learning enables the discovery of a potent and selective ripk1 inhibitor
Yueshan Li, Liting Zhang, Yifei Wang, Jun Zou, Ruicheng Yang, Xinling Luo, Chengyong Wu, Wei Yang, Chenyu Tian, Haixing Xu, Falu Wang, Xin Yang, Linli Li, and Shengyong Yang · 2022
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Retrognn: Fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski, Paweł Włodarczyk-Pruszyński, Yoshua Bengio, and Marwin Segler · 2022
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Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Arman A. Sadybekov, Anastasiia V. Sadybekov, Yongfeng Liu, Christos Iliopoulos-Tsoutsouvas, Xi-Ping Huang, Julie Pickett, Blake Houser, Nilkanth Patel, Ngan K. Tran, Fei Tong, Nikolai Zvonok, Manish K. Jain, Olena Savych, Dmytro S. Radchenko, Spyros P. Nikas, Nicos A. Petasis, Yurii S. Moroz, Bryan L. Roth, Alexandros Makriyannis, and Vsevolod Katritch · 2022
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Deep learning driven de novo drug design based on gastric proton pump structures
Kazuhiro Abe, Mami Ozako, Miki Inukai, Yoe Matsuyuki, Shinnosuke Kitayama, Chisato Kanai, Chiaki Nagai, Chai C Gopalasingam, Christoph Gerle, Hideki Shigematsu, Nariyoshi Umekubo, Satoshi Yokoshima, and Atsushi Yoshimori · 2023
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Virtualflow 2.0 - the next generation drug discovery platform enabling adaptive screens of 69 billion molecules
Christoph Gorgulla, AkshatKumar Nigam, Matt Koop, Suleyman Selim Cinaroglu, Christopher Secker, Mohammad Haddadnia, Abhishek Kumar, Yehor S. Malets, Alexander Hasson, Minkai Li, Ming Tang, Roni Levin-Konigsberg, Dmitry Radchenko, Aditya Kumar, Minko Gehev, Pierre-Yves Aquilanti, Henry Gabb, Amr A. Alhossary, Gerhard Wagner, Alán Aspuru-Guzik, Yurii S. Moroz, Konstantin Fackeldey, and Haribabu Arthanari · 2023
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Gps++: Reviving the art of message passing for molecular property prediction
Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew Fitzgibbon, Shenyang Huang, et al · 2023
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