2018/04/06 by Naruki Yoshikawa, Kei Terayama, Teruki Honma +2 · 1 citation
Physics and Astronomy · Biochemistry, Genetics and Molecular Biology · #physics.chem-ph #q-bio.BM
paper · pdf · doi:10.1246/cl.180665
published as Chemistry Letters, 47(11), 1431-1434 (2018)
arxiv created 2018/04/06 · arxiv updated 2018/10/31
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.