Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design.

ABSTRACT:

Motivation

In the field of pharmacochemistry, it is a time-consuming and expensive process for the new drug development. The existing drug design methods face a significant challenge in terms of generation efficiency and quality.

Results

In this paper, we proposed a novel molecular generation strategy and optimization based on A2C reinforcement learning. In molecular generation strategy, we adopted transformer-DNN to retain the scaffolds advantages, while accounting for the generated molecules' similarity and internal diversity by dynamic parameter adjustment, further improving the overall quality of molecule generation. In molecular optimization, we introduced heterogeneous parallel supercomputing for large-scale molecular docking based on message passing interface communic

SUBMITTER: Wang Q 

PROVIDER: S-EPMC10689670 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature