Tang Yat T, Marshall Garland R
Center for Computational Biology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
Methods Mol Biol. 2011;716:1-22. doi: 10.1007/978-1-61779-012-6_1.
The identification of small drug-like compounds that selectively inhibit the function of biological targets has historically been a major focus in the pharmaceutical industry, and in recent years, has generated much interest in academia as well. Drug-like compounds are valuable as chemical genetics tools to probe biological pathways in a reversible, dose- and time-dependent manner for drug target identification. In addition, small molecule compounds can be used to characterize the shape and charge preferences of macromolecular binding sites, for both structure-based and ligand-based drug design. High-throughput screening is the most common experimental method used to identify lead compounds. Because of the cost, time, and resources required for performing high-throughput screening for compound libraries, the use of alternative strategies is necessary for facilitating lead discovery. Virtual screening has been successful in prioritizing large chemical libraries to identify experimentally active compounds, serving as a practical and effective alternative to high-throughput screening. Methodologies used in virtual screening such as molecular docking and scoring have advanced to the point where they can rapidly and accurately identify lead compounds in addition to predicting native binding conformations. This chapter provides instructions on how to perform a virtual screen using freely available tools for structure-based lead discovery.
鉴定能够选择性抑制生物靶点功能的类药物小分子化合物,一直以来都是制药行业的主要关注点,近年来在学术界也引起了广泛关注。类药物化合物作为化学遗传学工具具有重要价值,可用于以可逆、剂量和时间依赖性方式探究生物途径,以确定药物靶点。此外,小分子化合物可用于表征基于结构和基于配体的药物设计中大分子结合位点的形状和电荷偏好。高通量筛选是用于鉴定先导化合物的最常见实验方法。由于对化合物库进行高通量筛选需要成本、时间和资源,因此有必要采用替代策略来促进先导化合物的发现。虚拟筛选已成功地对大型化学库进行优先级排序,以鉴定具有实验活性的化合物,成为高通量筛选的一种实用且有效的替代方法。虚拟筛选中使用的方法,如分子对接和评分,已经发展到除了预测天然结合构象外,还能快速准确地鉴定先导化合物的程度。本章提供了有关如何使用免费可用工具进行基于结构的先导化合物发现虚拟筛选的指导。