Suppr超能文献

评估用于中枢神经系统药物研发的分子支架。

Assessing molecular scaffolds for CNS drug discovery.

作者信息

Mayol-Llinàs Joan, Nelson Adam, Farnaby William, Ayscough Andrew

机构信息

School of Chemistry, University of Leeds, Leeds LS2 9JT, UK.

School of Chemistry, University of Leeds, Leeds LS2 9JT, UK.

出版信息

Drug Discov Today. 2017 Jul;22(7):965-969. doi: 10.1016/j.drudis.2017.01.008. Epub 2017 Jan 20.

Abstract

There is a need for high-quality screening collections that maximise hit rate and minimise the time taken in lead optimisation to derive a candidate drug. Identifying and accessing molecules that meet these criteria is a challenge. Within central nervous system (CNS)-focused drug discovery, this challenge is heightened by the requirement for lead compounds to cross the blood-brain barrier. Herein, we demonstrate use of a multiparameter optimisation tool to prioritise the synthesis of molecular scaffolds that, when subsequently decorated, yield screening compounds with experimentally determined properties that align with CNS lead generation needs. Prospective use of this CNS Lead Multiparameter Optimisation (MPO) scoring protocol can guide the further development of novel synthetic methodologies to access CNS-relevant and lead-like chemical space.

摘要

需要高质量的筛选集,以最大限度地提高命中率,并在先导优化过程中尽量减少获得候选药物所需的时间。识别和获取符合这些标准的分子是一项挑战。在以中枢神经系统(CNS)为重点的药物发现中,由于先导化合物需要穿过血脑屏障,这一挑战更加突出。在此,我们展示了使用多参数优化工具对分子支架的合成进行优先级排序,这些分子支架在随后进行修饰时,会产生具有实验确定性质的筛选化合物,这些性质与中枢神经系统先导化合物生成需求相一致。这种中枢神经系统先导多参数优化(MPO)评分方案的前瞻性应用可以指导新型合成方法的进一步发展,以进入与中枢神经系统相关的类先导化学空间。

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