{"title":"人工智能在基于计算结构的gpcr药物发现中与物理学相结合。","authors":"Mayako Michino, Jeremie Vendome, Irina Kufareva","doi":"10.1038/s44386-025-00019-0","DOIUrl":null,"url":null,"abstract":"<p><p>G protein-coupled receptors (GPCRs) are a prominent class of therapeutic targets for which structure-based drug discovery (SBDD) has traditionally been challenging to apply. However, recent artificial intelligence (AI)-powered breakthroughs have opened new avenues. Here, we discuss the impact of computational models on hit discovery and lead optimization for GPCRs. We also provide best practices for generating and validating predictive models for prospective use.</p>","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"2 1","pages":"16"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226350/pdf/","citationCount":"0","resultStr":"{\"title\":\"AI meets physics in computational structure-based drug discovery for GPCRs.\",\"authors\":\"Mayako Michino, Jeremie Vendome, Irina Kufareva\",\"doi\":\"10.1038/s44386-025-00019-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>G protein-coupled receptors (GPCRs) are a prominent class of therapeutic targets for which structure-based drug discovery (SBDD) has traditionally been challenging to apply. However, recent artificial intelligence (AI)-powered breakthroughs have opened new avenues. Here, we discuss the impact of computational models on hit discovery and lead optimization for GPCRs. We also provide best practices for generating and validating predictive models for prospective use.</p>\",\"PeriodicalId\":520448,\"journal\":{\"name\":\"NPJ drug discovery\",\"volume\":\"2 1\",\"pages\":\"16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226350/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NPJ drug discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s44386-025-00019-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ drug discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s44386-025-00019-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/3 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
AI meets physics in computational structure-based drug discovery for GPCRs.
G protein-coupled receptors (GPCRs) are a prominent class of therapeutic targets for which structure-based drug discovery (SBDD) has traditionally been challenging to apply. However, recent artificial intelligence (AI)-powered breakthroughs have opened new avenues. Here, we discuss the impact of computational models on hit discovery and lead optimization for GPCRs. We also provide best practices for generating and validating predictive models for prospective use.
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