Orestis A. Ntintas, Theodoros Daglis, Vassilis G. Gorgoulis
{"title":"利用深度学习构建优化配体。","authors":"Orestis A. Ntintas, Theodoros Daglis, Vassilis G. Gorgoulis","doi":"10.1038/s43588-024-00725-1","DOIUrl":null,"url":null,"abstract":"A recent study proposes DeepBlock, a deep learning-based approach for generating ligands with targeted properties, such as low toxicity and high affinity with the given target. This approach outperforms existing methods in the field while maintaining synthetic accessibility and drug-likeness.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"809-810"},"PeriodicalIF":12.0000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing deep learning to build optimized ligands\",\"authors\":\"Orestis A. Ntintas, Theodoros Daglis, Vassilis G. Gorgoulis\",\"doi\":\"10.1038/s43588-024-00725-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recent study proposes DeepBlock, a deep learning-based approach for generating ligands with targeted properties, such as low toxicity and high affinity with the given target. This approach outperforms existing methods in the field while maintaining synthetic accessibility and drug-likeness.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"4 11\",\"pages\":\"809-810\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-024-00725-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00725-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Harnessing deep learning to build optimized ligands
A recent study proposes DeepBlock, a deep learning-based approach for generating ligands with targeted properties, such as low toxicity and high affinity with the given target. This approach outperforms existing methods in the field while maintaining synthetic accessibility and drug-likeness.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
