Darren J Hsu, Hao Lu, Aditya Kashi, Michael Matheson, John Gounley, Feiyi Wang, Wayne Joubert, Jens Glaser
{"title":"双重:高度精确的结构和亲和预测从序列的蛋白质配体复合物","authors":"Darren J Hsu, Hao Lu, Aditya Kashi, Michael Matheson, John Gounley, Feiyi Wang, Wayne Joubert, Jens Glaser","doi":"10.1177/10943420231201151","DOIUrl":null,"url":null,"abstract":"We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. Solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.","PeriodicalId":54957,"journal":{"name":"International Journal of High Performance Computing Applications","volume":"191 ","pages":"0"},"PeriodicalIF":3.5000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences\",\"authors\":\"Darren J Hsu, Hao Lu, Aditya Kashi, Michael Matheson, John Gounley, Feiyi Wang, Wayne Joubert, Jens Glaser\",\"doi\":\"10.1177/10943420231201151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. Solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.\",\"PeriodicalId\":54957,\"journal\":{\"name\":\"International Journal of High Performance Computing Applications\",\"volume\":\"191 \",\"pages\":\"0\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2023-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of High Performance Computing Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/10943420231201151\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of High Performance Computing Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/10943420231201151","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
TwoFold: Highly accurate structure and affinity prediction for protein-ligand complexes from sequences
We describe our development of ab initio protein-ligand binding pose prediction models based on transformers and binding affinity prediction models based on the neural tangent kernel (NTK). Folding both protein and ligand, the TwoFold models achieve efficient and quality predictions matching state-of-the-art implementations while additionally reconstructing protein structures. Solving NTK models points to a new use case for highly optimized linear solver benchmarking codes on HPC.
期刊介绍：
With ever increasing pressure for health services in all countries to meet rising demands, improve their quality and efficiency, and to be more accountable; the need for rigorous research and policy analysis has never been greater. The Journal of Health Services Research & Policy presents the latest scientific research, insightful overviews and reflections on underlying issues, and innovative, thought provoking contributions from leading academics and policy-makers. It provides ideas and hope for solving dilemmas that confront all countries.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
