文章基本信息

标题：Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors
本地全文：下载
作者：Xuanyi Li ; Yinqiu Xu ; Hequan Yao 等
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2020
卷号：12
期号：1
页码：1-13
DOI：10.1186/s13321-020-00446-3
出版社：BioMed Central
摘要：With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.
关键词：Recurrent neural networks;De novo molecular generation;Chemical space;Kinase inhibitors