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整合介导的 HSP90 抑制剂作为抗癌药物的定量模型预测富集:3D-QSAR 研究。

Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study.

机构信息

Division of Medicinal and Process Chemistry, Central Drug Research Institute, CSIR, Lucknow, 226001, India.

出版信息

Mol Divers. 2011 May;15(2):477-89. doi: 10.1007/s11030-010-9269-y. Epub 2010 Aug 26.

Abstract

The present study describes a systematic 3D-QSAR study consisting of pharmacophore modeling, docking, and integration of ligand-based and structure-based drug design approaches, applied on a dataset of 72 Hsp90 inhibitors as anti-cancer agents. The best pharmacophore model, with one H-bond donor (HBD), one H-bond acceptor (HBA), one hydrophobic_aromatic (Hy_Ar), and two hydrophobic_aliphatic (Hy_Al) features, was developed using the Catalyst/HypoGen algorithm on a training set of 35 compounds. The model was further validated using test set, external set, Fisher's randomization method, and ability of the pharmacophoric features to complement the active site amino acids. Docking analysis was performed using Hsp90 chaperone (PDB-Id: 1uyf) along with water molecules reported to be crucial for binding and catalysis (Sgobba et al. ChemMedChem 4:1399-1409, 2009). Furthermore, an integration of the ligand-based as well as structure-based drug design approaches was done leading to the integrated model, which was found to be superior over the best pharmacophore model in terms of its predictive ability on internal [integrated model 2: R ((train)) = 0.954, R ((test)) = 0.888; Hypo-01: R ((train)) = 0.912 and R ((test)) = 0.819] as well as on external data set [integrated model 2: R ((ext.set)) = 0.801; Hypo-01: R ((ext.set)) = 0.604].

摘要

本研究描述了一项系统的 3D-QSAR 研究,该研究包括基于配体和基于结构的药物设计方法的药效团建模、对接和整合,应用于一组 72 种作为抗癌剂的 Hsp90 抑制剂。使用 Catalyst/HypoGen 算法在一个 35 个化合物的训练集上开发了一个具有一个氢键供体 (HBD)、一个氢键受体 (HBA)、一个疏水芳环 (Hy_Ar) 和两个疏水脂肪族 (Hy_Al) 特征的最佳药效团模型。该模型进一步通过测试集、外部集、Fisher 随机化方法以及药效团特征补充活性位点氨基酸的能力进行验证。对接分析使用 Hsp90 伴侣 (PDB-ID: 1uyf) 以及据报道对结合和催化至关重要的水分子 (Sgobba 等人,ChemMedChem 4:1399-1409, 2009) 进行。此外,还进行了基于配体和基于结构的药物设计方法的整合,得到了整合模型,该模型在内部 [整合模型 2: R ((train)) = 0.954, R ((test)) = 0.888; Hypo-01: R ((train)) = 0.912 和 R ((test)) = 0.819] 和外部数据集 [整合模型 2: R ((ext.set)) = 0.801; Hypo-01: R ((ext.set)) = 0.604] 上的预测能力均优于最佳药效团模型。

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