Computational Drug Discovery Group, CMBI, Radboud University Nijmegen, Geert Grooteplein Zuid 26-28, 6525 GA, Nijmegen, The Netherlands.
J Chem Inf Model. 2012 Jun 25;52(6):1607-20. doi: 10.1021/ci2005274. Epub 2012 Jun 13.
The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.
药效团概念在计算机辅助药物设计（CADD）中具有核心重要性，主要是因为它在药物化学中的成功应用，尤其是高通量虚拟筛选（HTVS）。药效团定义的简单性使得配体和受体之间的分子相互作用的复杂性可以简化为少数特征。有许多药效团筛选软件可用，因此，探索这些工具在应用于不同生物系统时的行为是非常有趣的。在这项工作中，我们使用默认设置，对八个药效团筛选算法（Catalyst、Unity、LigandScout、Phase、Pharao、MOE、Pharmer 和 POT）进行了比较分析，以用于针对四个不同生物靶标的典型 HTVS 活动。本文所呈现的结果表明，每个药效团筛选工具的性能可能与诸如结合口袋的特征、特定药效团特征的使用以及在药物发现管道的特定步骤/上下文中使用这些技术等因素密切相关。基于 RMSD 的评分函数的算法能够更准确地预测更多化合物构象作为基于叠加的评分函数。然而，基于叠加的评分函数的正确预测化合物构象与错误预测化合物构象的比例更好，并且在化合物库富集方面也能确保更好的性能。虽然这些观察结果的组合可用于为特定的虚拟筛选项目选择最合适的算法类别，但我们注意到药效团算法通常同样有效，在这方面，我们还分析了如何将药效团算法组合在一起以提高命中化合物鉴定的成功率。这项研究为药效团搜索算法领域的进一步发展提供了有价值的基准集，例如通过使用构象预测和化合物库富集标准。
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