Integrating docking scores and key interaction profiles to improve the accuracy of molecular docking: towards novel B-RafV600E inhibitors,RSC Medicinal Chemistry

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Integrating docking scores and key interaction profiles to improve the accuracy of molecular docking: towards novel B-RafV600E inhibitors
RSC Medicinal Chemistry ( IF 4.1 ) Pub Date : 2017-07-24 00:00:00 , DOI: 10.1039/c7md00229g
Chun-Qi Hu _{1,

2,

3,

4,

5} , Kang Li _{1,

2,

3,

4,

5} , Ting-Ting Yao _{1,

2,

3,

4,

5} , Yong-Zhou Hu _{1,

2,

3,

4,

5} , Hua-Zhou Ying _{1,

2,

3,

4,

5} , Xiao-Wu Dong _{1,

2,

3,

4,

5}

Affiliation

A set of ninety-eight B-Raf^V600E inhibitors was used for the development of a molecular docking based QSAR model using linear and non-linear regression models. The integration of docking scores and key interaction profiles significantly improved the accuracy of the QSAR models, providing reasonable statistical parameters (R_train² = 0.935, R_test² = 0.728 and Q_CV² = 0.905). The established MD-SVR (molecular docking based SMV regression) model as well as model screening of a natural product database was carried out and two natural products (quercetin and myricetin) with good prediction activities were biologically evaluated. Both compounds exhibited promising B-Raf^V600E inhibitory activities (IC^Quercetin₅₀ = 7.59 μM and IC^Myricetin₅₀ = 1.56 μM), suggesting a high reliability and good applicability of the established MD-SVR model in the future development of B-Raf^V600E inhibitors with high efficacy.

中文翻译：

整合对接分数和关键的相互作用谱以提高分子对接的准确性：针对新型B-Raf ^V600E抑制剂

一组九十八个B-Raf ^V600E抑制剂用于使用线性和非线性回归模型开发基于分子对接的QSAR模型。对接分数和关键交互配置文件的集成显着提高了QSAR模型的准确性，提供了合理的统计参数（R_训练² = 0.935，R_检验² = 0.728和Q _CV²= 0.905）。建立建立的MD-SVR（基于分子对接的SMV回归）模型以及天然产物数据库的模型筛选，并对具有良好预测活性的两种天然产物（槲皮素和杨梅素）进行了生物学评估。两种化合物均显示出有希望的B-Raf ^V600E抑制活性（IC^槲皮素₅₀ = 7.59μM和IC^杨梅素₅₀ = 1.56μM），表明已建立的MD-SVR模型在B-Raf ^V600E抑制剂的未来开发中具有很高的可靠性和良好的适用性。疗效高。

更新日期：2017-09-21

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