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探索新的靶点空间:是否需要将高通量对接与基于配体的相似性搜索相结合?

Exploring novel target space: a need to partner high throughput docking and ligand-based similarity searches?

作者信息

Shanmugasundaram Kumaran, Rigby Alan C

机构信息

Division of Molecular and Vascular Medicine, Center for Vascular Biology Research, Department of Medicine, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA 02215, USA.

出版信息

Comb Chem High Throughput Screen. 2009 Dec;12(10):984-99. doi: 10.2174/138620709789824709.

Abstract

Recent advances in combinatorial chemistry (CC) and High throughput screening (HTS) approaches for use in drug discovery have made it possible to synthesize and/or screen large repositories of chemically diverse scaffolds in search of small molecules that disrupt or regulate macromolecular function. Although successful in the discovery of novel therapeutics this approach is both costly and time consuming. In silico computer aided drug discovery (CADD) approaches including; structure based virtual screening (SBVS) or high throughput docking (HTD) and/or ligand based virtual screening (LBVS) are areas experiencing renewed interest both in the pharmaceutical industry and academia. The emerging success of these approaches alone or partnered with HTS platforms in search of, and/or optimization of, novel therapeutic compounds represents a potential approach for the identification of therapies that target novel space. Here we will discuss how LBVS has been and continues to be partnered with HTS in early stage compound identification and/or triage. We will also provide a significant overview of how SBVS when partnered with LBVS can overcome the limitations inherent to each approach when used alone. We will discuss this partnered approach in the context of both traditional drug discovery targets and provide thoughts on its applicability to study novel chemical space including protein-protein and/or other historical intractable interfaces.

摘要

用于药物发现的组合化学(CC)和高通量筛选(HTS)方法的最新进展,使得合成和/或筛选大量化学结构多样的支架库以寻找能够破坏或调节大分子功能的小分子成为可能。尽管这种方法在发现新型治疗药物方面取得了成功,但它既昂贵又耗时。计算机辅助药物发现(CADD)方法包括基于结构的虚拟筛选(SBVS)或高通量对接(HTD)和/或基于配体的虚拟筛选(LBVS),这些领域在制药行业和学术界都重新引起了人们的兴趣。这些方法单独使用或与HTS平台合作寻找和/或优化新型治疗化合物的新成功,代表了一种识别针对新领域的治疗方法的潜在途径。在这里,我们将讨论LBVS如何在早期化合物识别和/或分类中与HTS合作,并且一直保持合作。我们还将对SBVS与LBVS合作时如何克服单独使用每种方法时固有的局限性进行全面概述。我们将在传统药物发现靶点的背景下讨论这种合作方法,并对其在研究包括蛋白质-蛋白质和/或其他以往难以处理的界面在内的新型化学空间中的适用性提出看法。

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