Hu Chun-Qi, Li Kang, Yao Ting-Ting, Hu Yong-Zhou, Ying Hua-Zhou, Dong Xiao-Wu
Zhejiang Province Key Laboratory of Anti-Cancer Drug Research , College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , P.R. China . Email:
College of Chemistry & Chemical Engineering , Shaoxing University , Shaoxing , P.R. China.
Medchemcomm. 2017 Jul 24;8(9):1835-1844. doi: 10.1039/c7md00229g. eCollection 2017 Sep 1.
A set of ninety-eight B-Raf 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 ( = 0.935, = 0.728 and = 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 inhibitory activities (ICQuercetin50 = 7.59 μM and ICMyricetin50 = 1.56 μM), suggesting a high reliability and good applicability of the established MD-SVR model in the future development of B-Raf inhibitors with high efficacy.
使用一组98种B-Raf抑制剂，通过线性和非线性回归模型开发基于分子对接的QSAR模型。对接分数和关键相互作用谱的整合显著提高了QSAR模型的准确性，提供了合理的统计参数（R² = 0.935，Q² = 0.728和RMSE = 0.905）。进行了建立的MD-SVR（基于分子对接的支持向量回归）模型以及天然产物数据库的模型筛选，并对两种具有良好预测活性的天然产物（槲皮素和杨梅素）进行了生物学评估。两种化合物均表现出有前景的B-Raf抑制活性（槲皮素IC50 = 7.59 μM，杨梅素IC50 = 1.56 μM），表明所建立的MD-SVR模型在未来高效B-Raf抑制剂的开发中具有高可靠性和良好适用性。
