Journal
SCIENTIFIC REPORTS
Volume 10, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41598-020-78537-2
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Funding
- Bio-Synergy Research Project [2017M3A9C4065952]
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1A2C1007951]
- KAIST Mobile Clinic Module Project [MCM-2020-N11200215]
- National Research Foundation of Korea [2019R1A2C1007951] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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 D-4 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.
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