Suppr超能文献

基于进化结构的从头药物设计与优化的标记片段方法

Tagged fragment method for evolutionary structure-based de novo lead generation and optimization.

作者信息

Liu Qian, Masek Brian, Smith Karl, Smith Julian

机构信息

Tripos, Inc., 1699 South Hanley Road, St. Louis, Missouri 63144, USA.

出版信息

J Med Chem. 2007 Nov 1;50(22):5392-402. doi: 10.1021/jm070750k. Epub 2007 Oct 6.

Abstract

Here we describe a computer-assisted de novo drug design method, EAISFD, which combines the de novo design engine EA-Inventor with a scoring function featuring the molecular docking program Surflex-Dock. This method employs tagged fragments, which are preserved substructures in EA-Inventor used for base fragment matching in Surflex-Dock, for constructing ligand structures under specific binding motifs. In addition, a target score mechanism is adopted that allows EAISFD to deliver a diverse set of desired structures. This method can be used to design novel ligand scaffolds (lead generation) or to optimize attachments on a fixed scaffold (lead optimization). EAISFD has successfully suggested many known inhibitor scaffolds as well as a number of new scaffold types when applied to p38 MAP kinase.

摘要

在此,我们描述了一种计算机辅助的从头药物设计方法——EAISFD,它将从头设计引擎EA-Inventor与具有分子对接程序Surflex-Dock的评分函数相结合。该方法采用标记片段,这些片段是EA-Inventor中用于Surflex-Dock碱基片段匹配的保留子结构,用于在特定结合基序下构建配体结构。此外,还采用了目标评分机制,使EAISFD能够提供多种所需结构。该方法可用于设计新型配体支架(先导化合物生成)或优化固定支架上的连接(先导化合物优化)。当应用于p38丝裂原活化蛋白激酶时,EAISFD成功地提出了许多已知的抑制剂支架以及一些新的支架类型。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验