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- W3100839802 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." @default.
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- W3100839802 date "2018-11-05" @default.
- W3100839802 modified "2023-10-12" @default.
- W3100839802 title "Population-based De Novo Molecule Generation, Using Grammatical Evolution" @default.
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- W3100839802 doi "https://doi.org/10.1246/cl.180665" @default.
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