N. Yoshikawa, Kei Terayama, T. Honma, Kenta Oono, Koji Tsuda
{"title":"基于群体的从头分子生成,使用语法进化","authors":"N. Yoshikawa, Kei Terayama, T. Honma, Kenta Oono, Koji Tsuda","doi":"10.1246/cl.180665","DOIUrl":null,"url":null,"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.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"70","resultStr":"{\"title\":\"Population-based de novo molecule generation, using grammatical evolution\",\"authors\":\"N. Yoshikawa, Kei Terayama, T. Honma, Kenta Oono, Koji Tsuda\",\"doi\":\"10.1246/cl.180665\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":8439,\"journal\":{\"name\":\"arXiv: Chemical Physics\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"70\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: Chemical Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1246/cl.180665\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Chemical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1246/cl.180665","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Population-based de novo molecule generation, using grammatical evolution
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.