Population-based de novo molecule generation, using grammatical evolution

TL;DR

ChemGE is a population-based grammatical evolution method that concurrently generates diverse molecules and evaluates them with multiple simulators, improving drug candidate discovery efficiency and molecular diversity.

Contribution

This paper introduces ChemGE, a novel population-based grammatical evolution approach enabling parallel molecule generation and evaluation, enhancing diversity and efficiency in drug design.

Findings

Generated hundreds of high-affinity molecules with ChemGE

Achieved better molecular diversity than known binders

Demonstrated effectiveness in docking experiments with thymidine kinase

Abstract

Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have problems in simulation concurrency and molecular diversity. Most methods generate one molecule at a time and do not allow multiple simulators to run simultaneously. Additionally, better molecular diversity could boost the success rate in the subsequent drug discovery process. We propose a new population-based approach using grammatical evolution named ChemGE. In our method, a large population of molecules are updated concurrently and evaluated by multiple simulators in parallel. In docking experiments with thymidine kinase, ChemGE succeeded in generating hundreds of high-affinity molecules whose diversity is better than that of known inding molecules in DUD-E.