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Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors
Xi Chen,Lian-sheng Qiao,Yi-lian Cai,Yan-ling Zhang,Gong-yu Li
Author NameAffiliationE-mail
Xi Chen Key Laboratory of TCM Foundation and New Drug Research School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China  
Lian-sheng Qiao Key Laboratory of TCM Foundation and New Drug Research School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China  
Yi-lian Cai Key Laboratory of TCM Foundation and New Drug Research School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China  
Yan-ling Zhang Key Laboratory of TCM Foundation and New Drug Research School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China zhangyanling@bucm.edu.cn 
Gong-yu Li Key Laboratory of TCM Foundation and New Drug Research School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China  
Abstract:
The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 (CYP1A2) inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biolog-ical activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identi ed to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding a n-ity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug inter-actions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.
Key words:  Support vector machine  Support vector regression  Molecular docking  CYP1A2 inhibitor
FundProject:
基于支持向量机、支持向量回归和分子对接的CYP450 1A2抑制剂的发现研究
陈茜,乔连生,蔡漪涟,张燕玲,李贡宇
摘要:
支持向量机,支持向量回归和分子对接的计算方法已广泛应用于化合物的药理活性计算。为了提高计算的准确性和可靠性,拟以细胞色素P450酶1A2为研究载体,运用建立的联合SVM-SVR-Docking计算模型预测潜在的CYP1A2抑制剂。其中,建立的最优SVM定性模型训练集,内部测试集和外部测试集的准确率分别为99.432%,97.727%和91.667%。最优SVR定量模型训练集和测试集的R和MSE分别为0.763,0.013和0.753,0.056。实验表明两个模型具有较高的准确性和可靠性。通过对SVM和SVR模型结果的比较分析,发现连接性指数、分子构成描述符和官能团数目等分子描述符可能与CYP1A2抑制剂的辨识和活性预测密切相关。随后利用分子对接技术分析化合物与CYP1A2的结合构象及相互作用的稳定性。形成氢键相互作用的关键氨基酸包括THR124,ASP320;形成疏水相互作用的关键氨基酸包括ALA317和GLY316。所获得模型可用于天然产物化学成分中CYP1A2潜在抑制剂的活性计算及其介导的药物-药物相互作用预测提供理论指导,也为合理联合用药提供一定参考。共获得20个对CYP1A2具有潜在抑制活性的化合物。部分结果与文献结果相互印证,进一步说明了模型的准确性和联合计算策略的可靠性.
关键词:  支持向量机  支持向量回归  分子对接  CYP1A2抑制剂
DOI:10.1063/1674-0068/29/cjcp1603039
分类号: