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支持向量机在药物代谢和药物的QSAR模型中的应用
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摘要
作为一种数据挖掘的新方法,支持向量机已被应用于许多不同领域。我们采用SVM方法,筛选适当的分子描述符,选择并优化SVM参数,进行了CVP3A4抑制剂生物活性等数据的QSAR建模。模型经交叉验证和预测集法验证,同时偏最小二乘法和人工神经网络方法也进行建模作为比照。最终SVM方法的交叉验证相关性系数达到0.92而预测集的相关性系数为0.73.各项统计标准均优于其它回归方法所得的模型,证明SVM方法是一种较为可靠的QSAR/QSPR回归工具。同时,本文还讨论了各种回归方法对P450的Km值的预测以及嘧啶类抗疟疾药物的3D-QSAR。从各类文献中收集到不同P450酶的数百个底物的Km值数据,采用不同的回归方法分别建立了QSAR模型,并对不同的回归方法进行了比较。疟疾每年造成的死亡超过百万,据报道恶性虐原虫(Plasmodium falciparum)的甲硫氨酸氨基肽酶可能是一个潜在的抗疟药物靶点,因此我们对一系列嘧啶类衍生物进行了3D定量构效关系研究,CoMFA方法取得了不错的结果,而CoMSIA的模型则不够理想。CoMFA模型的训练集交叉验证系数q2=0.617,预测集相关性系数为0.631,说明模型的预测能力较好,对于新的抗疟药的设计可能会有所帮助。
The support vector machine (SVM) is a new method of data mining, which has been widely applied in many fields. Using this method we constructed several computational model of CYP3A4 inhibition based on the experimental IC50 values of 42 inhibitors and few molecular descriptors represented the physiochemical and structural property of compound. These models were further evaluated by the cross validation and the independent test set. The model with the highest predictive ability reached the cross validated correlation coefficient r2 of 0.92 and the predictive coefficient r2 of test set of 0.73. The predictive ability of model obtained using SVM is better compared with the models derived from the ANN and PLS methods. The results indicate that SVM can also be severed as a promising statistic tool for quantitative structure- property/ activity relationship (QSPR/QSAR) studies. We also built some model to predict the Km values of P450 substrates and a 3D-QSAR model for a series of pyrimidine derivatives. We selected hundreds of Km values of different P450 enzyme substrates from many references. We used different regression methods to build QSAR models and compare SVM models with the others. Malaria kills over 1 million people annually. It was reported that the Plasmodium falciparum methionine aminopeptidase 1b inhibitors could be potential antimalaria agents. This three dimensional quantitative structure activity relationship(3D-QSAR) model for a series of pyrimidine derivatives was developed to gain insights into design for potential new antimalaria agents. It is found the Comparative Molecular Field Analysis (CoMFA) method gives good results while the Comparative Molecular Similarity Indices Analysis(CoMSIA) is less satisfactory. The CoMFA method yields a cross–validated correlation coefficient q2 =0.617, non-cross-validated R2=0.975, SE(Standard error)=0.086 and the F ratio=94.518. It indicates that the model possesses a high predictability. The model we build may be of value in facilitating design of new potent antimalaria agents.
引文
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