Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors

Abstract We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target pr...

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Autores principales:	Woosung Jeon, Dongsup Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/183053f197494873ae10dd6fed21e202
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spelling	oai:doaj.org-article:183053f197494873ae10dd6fed21e2022021-12-02T12:42:28ZAutonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors10.1038/s41598-020-78537-22045-2322https://doaj.org/article/183053f197494873ae10dd6fed21e2022020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78537-2https://doaj.org/toc/2045-2322Abstract We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target protein structure and directly modifies ligand structures to obtain higher predicted binding affinity for the target protein without any other training data. Using MORLD, we were able to generate potential novel inhibitors against discoidin domain receptor 1 kinase (DDR1) in less than 2 days on a moderate computer. We also demonstrated MORLD’s ability to generate predicted novel agonists for the D4 dopamine receptor (D4DR) from scratch without virtual screening on an ultra large compound library. The free web server is available at http://morld.kaist.ac.kr .Woosung JeonDongsup KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Woosung Jeon Dongsup Kim Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
description	Abstract We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target protein structure and directly modifies ligand structures to obtain higher predicted binding affinity for the target protein without any other training data. Using MORLD, we were able to generate potential novel inhibitors against discoidin domain receptor 1 kinase (DDR1) in less than 2 days on a moderate computer. We also demonstrated MORLD’s ability to generate predicted novel agonists for the D4 dopamine receptor (D4DR) from scratch without virtual screening on an ultra large compound library. The free web server is available at http://morld.kaist.ac.kr .
format	article
author	Woosung Jeon Dongsup Kim
author_facet	Woosung Jeon Dongsup Kim
author_sort	Woosung Jeon
title	Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
title_short	Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
title_full	Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
title_fullStr	Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
title_full_unstemmed	Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
title_sort	autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/183053f197494873ae10dd6fed21e202
work_keys_str_mv	AT woosungjeon autonomousmoleculegenerationusingreinforcementlearninganddockingtodeveloppotentialnovelinhibitors AT dongsupkim autonomousmoleculegenerationusingreinforcementlearninganddockingtodeveloppotentialnovelinhibitors
_version_	1718393669852069888

Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors

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