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.

通过机器学习和分子模拟进行的自动设计已经表现出生成新的和有前途的新药物候选物的显着能力。本文提出了使用语法进化的一种新的基于群体的方法。在 thymidine 激酶的对接实验中，ChemGE 成功生成了数百个高亲和力的分子，其多样性优于 DUD-E 中已知的分子。