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基于片段的药物设计在开发针对中枢神经系统靶点的新型先导化合物中的作用。

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets.

作者信息

Wasko Michael J, Pellegrene Kendy A, Madura Jeffry D, Surratt Christopher K

机构信息

Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA.

Department of Chemistry and Biochemistry, Center for Computational Sciences, Bayer School of Natural and Environmental Sciences, Duquesne University , Pittsburgh, PA , USA.

出版信息

Front Neurol. 2015 Sep 11;6:197. doi: 10.3389/fneur.2015.00197. eCollection 2015.

DOI:10.3389/fneur.2015.00197
PMID:26441817
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4566055/
Abstract

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for "growing" the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

摘要

数亿美元被投入到单一药物的研发中。先导化合物的开发是一个充满新设计策略的领域。传统上,治疗性先导候选物是通过高通量体外药理学筛选来发现的,这是一种用于检测数千种化合物的昂贵方法。最近,虚拟筛选(VS)对这种方法进行了补充,它利用靶蛋白的计算机模型来缩小对可能先导物的搜索范围。VS的一个变体是基于片段的药物设计(FBDD),这是一种新兴的计算机辅助先导发现方法,它将低分子量片段而非完整化合物引入受体模型的结合口袋。这些片段作为“培育”先导候选物的起点。本文综述了目前针对中枢神经系统(CNS)靶点的虚拟FBDD研究工作,以及一种基于规则的优化策略,即在三维受体结合口袋中以片段为支架生成新分子。这个过程不仅特别强调生成可合成的分子,还暴露出一些值得解决的计算问题。基于片段的方法为治疗性先导物的发现和优化提供了一种可行的、成本相对较低的替代方案,可应用于CNS靶点以增强当前的设计策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/472d787ad4e6/fneur-06-00197-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/70d4d6961961/fneur-06-00197-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/506787056b35/fneur-06-00197-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/14bebbb6ea10/fneur-06-00197-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/27e068ef6848/fneur-06-00197-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/472d787ad4e6/fneur-06-00197-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/70d4d6961961/fneur-06-00197-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/506787056b35/fneur-06-00197-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/14bebbb6ea10/fneur-06-00197-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/27e068ef6848/fneur-06-00197-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303a/4566055/472d787ad4e6/fneur-06-00197-g005.jpg

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