  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.
