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一些B-RAF抑制剂的二芳基脲衍生物的定量构效关系(QSAR)研究。

A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors.

作者信息

Sadeghian-Rizi Sedighe, Sakhteman Amirhossein, Hassanzadeh Farshid

机构信息

Department of Medicinal Chemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran.

Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, I.R. Iran.

出版信息

Res Pharm Sci. 2016 Dec;11(6):445-453. doi: 10.4103/1735-5362.194869.

Abstract

In the current study, both ligand-based molecular docking and receptor-based quantitative structure activity relationships (QSAR) modeling were performed on 35 diaryl urea derivative inhibitors of B-RAF. In this QSAR study, a linear (multiple linear regressions) and a nonlinear (partial least squares least squares support vector machine (PLS-LS-SVM)) were used and compared. The predictive quality of the QSAR models was tested for an external set of 31 compounds, randomly chosen out of 35 compounds. The results revealed the more predictive ability of PLS-LS-SVM in analysis of compounds with urea structure. The selected descriptors indicated that size, degree of branching, aromaticity, and polarizability affected the inhibition activity of these inhibitors. Furthermore, molecular docking was carried out to study the binding mode of the compounds. Docking analysis indicated some essential H-bonding and orientations of the molecules in the active site.

摘要

在当前研究中,对35种B-RAF的二芳基脲衍生物抑制剂进行了基于配体的分子对接和基于受体的定量构效关系(QSAR)建模。在这项QSAR研究中,使用并比较了线性(多元线性回归)和非线性(偏最小二乘最小二乘支持向量机(PLS-LS-SVM))方法。对从35种化合物中随机选出的31种化合物的外部集测试了QSAR模型的预测质量。结果表明,PLS-LS-SVM在分析具有尿素结构的化合物方面具有更强的预测能力。所选描述符表明,大小、支化度、芳香性和极化率影响这些抑制剂的抑制活性。此外,进行了分子对接以研究化合物的结合模式。对接分析表明了分子在活性位点中的一些关键氢键和取向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020c/5168880/3c6265d0343c/RPS-11-445-g004.jpg

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