Zhong Shijun, Zhang Youping, Xiu Zhilong
University of Maryland, Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, 20 Penn Street, Baltimore, MD 21201, USA.
Curr Opin Drug Discov Devel. 2010 May;13(3):326-34.
The ranking of ligand docking poses according to certain scoring systems to identify the best fit is the most important step in virtual database screening for drug discovery. By focusing on method development strategy, this review provides possibilities for constructing rescoring approaches based on an overview of recent developments in the field. These developments can be classified into three categories. The first category involves a scaling approach that employs a factor to scale the primary scoring function. These scaling factors are defined with respect to the geometrical match between the location of a ligand and the target binding site, or defined according to a molecular weight distribution consistent with the empirical range of molecular weights of drug-like compounds. The second category involves consensus scoring approaches that use multiple scoring functions to rank the ligand poses retained in a docking procedure, based on the preliminary ranking according to a primary scoring function. The final category involves the addition of selected accuracy-oriented energy terms, such as the solvent effect and quantum mechanics/molecular mechanics treatments.
根据特定评分系统对配体对接姿势进行排序以确定最佳匹配，是药物发现虚拟数据库筛选中最重要的步骤。通过关注方法开发策略，本综述基于该领域的最新进展概述，为构建重新评分方法提供了可能性。这些进展可分为三类。第一类涉及一种缩放方法，该方法使用一个因子来缩放主要评分函数。这些缩放因子是根据配体位置与靶标结合位点之间的几何匹配来定义的，或者是根据与类药物化合物分子量的经验范围一致的分子量分布来定义的。第二类涉及共识评分方法，该方法基于根据主要评分函数的初步排名，使用多个评分函数对对接过程中保留的配体姿势进行排名。最后一类涉及添加选定的以准确性为导向的能量项，如溶剂效应和量子力学/分子力学处理。
