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基于试剂药效团指纹的组合文库设计。

Combinatorial library design from reagent pharmacophore fingerprints.

作者信息

Chen Hongming, Engkvist Ola, Blomberg Niklas

机构信息

DECS GCS Computational Chemistry, AstraZeneca R&D Mölndal, Mölndal, Sweden.

出版信息

Methods Mol Biol. 2011;685:135-52. doi: 10.1007/978-1-60761-931-4_7.

Abstract

Combinatorial and parallel chemical synthesis technologies are powerful tools in early drug discovery projects. Over the past couple of years an increased emphasis on targeted lead generation libraries and focussed screening libraries in the pharmaceutical industry has driven a surge in computational methods to explore molecular frameworks to establish new chemical equity. In this chapter we describe a complementary technique in the library design process, termed ProSAR, to effectively cover the accessible pharmacophore space around a given scaffold. With this method reagents are selected such that each R-group on the scaffold has an optimal coverage of pharmacophoric features. This is achieved by optimising the Shannon entropy, i.e. the information content, of the topological pharmacophore distribution for the reagents. As this method enumerates compounds with a systematic variation of user-defined pharmacophores to the attachment point on the scaffold, the enumerated compounds may serve as a good starting point for deriving a structure-activity relationship (SAR).

摘要

组合化学和平行化学合成技术是早期药物发现项目中的强大工具。在过去几年中,制药行业对靶向先导化合物生成库和聚焦筛选库的重视日益增加,推动了探索分子框架以建立新化学优势的计算方法的激增。在本章中,我们描述了一种在库设计过程中的补充技术,称为ProSAR,以有效覆盖给定支架周围可及的药效团空间。使用这种方法选择试剂,使得支架上的每个R基团对药效特征具有最佳覆盖。这是通过优化试剂的拓扑药效团分布的香农熵,即信息含量来实现的。由于该方法枚举了具有用户定义药效团到支架上连接点的系统变化的化合物,枚举的化合物可作为推导构效关系(SAR)的良好起点。

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