José Teófilo Moreira-Filho , Meryck Felipe Brito da Silva , Joyce Villa Verde Bastos Borba , Arlindo Rodrigues Galvão Filho , Eugene N Muratov , Carolina Horta Andrade , Rodolpho de Campos Braga , Bruno Junior Neves
{"title":"Artificial intelligence systems for the design of magic shotgun drugs","authors":"José Teófilo Moreira-Filho , Meryck Felipe Brito da Silva , Joyce Villa Verde Bastos Borba , Arlindo Rodrigues Galvão Filho , Eugene N Muratov , Carolina Horta Andrade , Rodolpho de Campos Braga , Bruno Junior Neves","doi":"10.1016/j.ailsci.2022.100055","DOIUrl":null,"url":null,"abstract":"<div><p>Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help <em>de novo</em> design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for <em>de novo</em> drug design and multi-target drug discovery.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"3 ","pages":"Article 100055"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence in the life sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667318522000253","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help de novo design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for de novo drug design and multi-target drug discovery.
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)