{"title":"分子对接中的深度图学习:进展与机遇","authors":"Norberto Sánchez-Cruz","doi":"10.1016/j.ailsci.2023.100062","DOIUrl":null,"url":null,"abstract":"<div><p>One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100062"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Deep graph learning in molecular docking: Advances and opportunities\",\"authors\":\"Norberto Sánchez-Cruz\",\"doi\":\"10.1016/j.ailsci.2023.100062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.</p></div>\",\"PeriodicalId\":72304,\"journal\":{\"name\":\"Artificial intelligence in the life sciences\",\"volume\":\"3 \",\"pages\":\"Article 100062\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial intelligence in the life sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667318523000065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence in the life sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667318523000065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep graph learning in molecular docking: Advances and opportunities
One of the main computational tools for structure-based drug discovery is molecular docking. Due to the natural representation of molecules as graphs (a set of nodes/atoms connected through edges/bonds), Deep Graph Learning has been successfully applied for multiple tasks on this area. This work presents an overview of Deep Graph Learning methods developed within this research field, as well as opportunities for future development.
Artificial intelligence in the life sciencesPharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
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