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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 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 .

SUBMITTER: Jeon W

PROVIDER: S-EPMC7744578 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Jeon Woosung W Kim Dongsup D

Scientific reports 20201216 1

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 ...[more]

PMID: 33328504

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