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靶点发现:迈向“更智能”的方法。

Hit finding: towards 'smarter' approaches.

作者信息

Langer Thierry, Hoffmann Rémy, Bryant Sharon, Lesur Brigitte

机构信息

Prestwick Chemical Parc d'Innovation, 67400 Illkirch, France.

出版信息

Curr Opin Pharmacol. 2009 Oct;9(5):589-93. doi: 10.1016/j.coph.2009.06.001. Epub 2009 Jul 1.

Abstract

Drug discovery is complex and risky, and the chances of success are low. One starting point to discover a new drug is the selective screening of a collection of high value and good quality compounds. Selection of compounds for screening is one of the challenging initial steps in the drug discovery process and is crucial for the success of the project. Optimal selection will enhance the chances of successful hit finding with regard to both number and quality of hits. Several scenarios for compound selection can be envisaged, and are primarily driven by knowledge of the target. Deciding the most appropriate scenario is important and appropriate software packages and chemoinformatics tools are available for these purposes. After screening, researchers may face challenges in selecting the best hits for further optimization. Numerous chemoinformatics tools have emerged recently to address challenges in hit analysis, prioritization and optimization.

摘要

药物研发复杂且风险高,成功几率很低。发现一种新药的一个起点是对一批高价值且高质量的化合物进行选择性筛选。筛选化合物是药物研发过程中具有挑战性的初始步骤之一,对项目的成功至关重要。最佳选择将在命中化合物的数量和质量方面提高成功发现活性物质的几率。可以设想几种化合物选择方案,这些方案主要由对靶点的了解驱动。确定最合适的方案很重要,并且有适用于这些目的的软件包和化学信息学工具。筛选后,研究人员在选择最佳活性物质进行进一步优化时可能会面临挑战。最近出现了许多化学信息学工具来应对活性物质分析、优先级排序和优化方面的挑战。

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