LabMol, Faculdade de Farmacia, Universidade Federal de Goias, 1a Av com Praça Universitaria, Setor Universitario, Goiania, Goias 74605-220, Brazil.
Curr Top Med Chem. 2013;13(9):1127-38. doi: 10.2174/1568026611313090010.
Pharmacophore approaches have evolved to be one of the most successful tools in drug discovery, especially since the past two decades. 3D pharmacophore methods are now commonly used as part of more complex workflows in drug discovery campaigns, and have been successfully and extensively applied in virtual screening (VS) approaches. This review provides a perspective of how to assess the performance of 3D pharmacophore models to be used in VS. Since 3D VS protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of modeling and external sets on the outcome of evaluations. Moreover, we highlight the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D pharmacophore-based virtual screening methods.
药效团方法已经发展成为药物发现中最成功的工具之一,尤其是在过去二十年中。3D 药效团方法现在通常作为药物发现活动中更复杂工作流程的一部分,并且已经在虚拟筛选 (VS) 方法中成功广泛应用。本文综述了如何评估用于 VS 的 3D 药效团模型的性能。由于 3D VS 方案通常通过其区分活性和非活性化合物的能力进行评估,因此我们总结了建模和外部集的组成和准备对评估结果的影响。此外,我们强调了经典富集参数和高级描述符对基于 3D 药效团的虚拟筛选方法性能的重要性。