Deep Reinforcement Learning and Docking Simulations for autonomous molecule generation in de novo Drug Design

Abstract

In medicinal chemistry programs, it is key to design and make compounds that are efficacious and safe. In this study, we developed a new deep Reinforcement learning-based compounds molecular generation method. Because chemical space is impractically large, and many existing generation models generate molecules that lack effectiveness, novelty and unsatisfactory molecular properties. Our proposed method-DeepRLDS, which integrates transformer network, balanced binary tree search and docking simulation based on super large-scale supercomputing, can solve these problems well. Experiments show that more than 96 of the generated molecules are chemically valid, 99 of the generated molecules are chemically novelty, the generated molecules have satisfactory molecular properties and possess a broader chemical space distribution.