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Abstract
This paper introduces MORLD, a computational method that autonomously generates and optimizes lead compounds for drug discovery. Combining reinforcement learning and docking simulations, MORLD requires only the target protein structure and an initial ligand (or can even start from scratch). It iteratively modifies the ligand structure to improve predicted binding affinity, demonstrated effectively against DDR1 kinase and D4 dopamine receptor within a short timeframe (less than two days). A free web server is available for public use.
Publisher
Scientific Reports
Published On
Oct 27, 2020
Authors
Woosung Jeon, Dongsup Kim
Tags
drug discovery
MORLD
reinforcement learning
binding affinity
docking simulations
lead compounds
kinase

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