Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

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.

基于支持向量回归和分子对接技术的中药CYP450 2E1抑制剂筛选

细胞色素P450酶(CYP450)的抑制是药物相互作用最常见的原因,对CYPs抑制剂早期预测的研究有助于减少药物相互作用导致的不良反应。CYP450 2E1(CYP2E1)是CYP450酶系中参与药物体内代谢的主要酶,具有广泛的药物代谢底物谱。该研究以CYP2E1为研究对象,基于32个CYP2E1抑制剂,建立支持向量回归模型(support vector regression,SVR),并用测试集数据对CYP2E1定量模型进行验证,获得CYP2E1抑制剂最优预测模型。该研究同时利用CDOCKER分析阳性化合物与活性口袋相互作用模式及氨基酸,建立CYP2E1抑制剂最优筛选模型。综合利用支持向量回归模型和分子对接预测模型筛选中药化学成分数据库(traditional Chinese medicine database,TCMD),提高了计算效率和结果的准确性。SVR预测模型初步得到6 376个中药化合物,通过分子对接进一步验证,最终获得247个对CYP2E1具有潜在抑制活性的中药化合物,其中部分已有实验证实其的确对CYP2E1具有抑制作用。该研究对CYP2E1酶抑制剂的研究可为CYP450抑制剂的虚拟筛选及其介导的药物不良反应预测提供指导,对临床合理用药提供一定的参考。