Combinations of fragment descriptors for improved prediction of CYP2C19 inhibitors

Abstract

The human cytochrome P450 (CYP450) superfamily plays an important role in drug–drug interactions, drug metabolism, and toxicity. Therefore, prediction of CYP450 inhibitors is extremely important in drug discovery and personal medicine. In this paper, characterized by fragment-based molecular hologram and MACCS descriptors, over 12,000 unique compounds with known CYP2C19 inhibitory activities were used to develop prediction models by partial least squares discriminant analysis (PLSDA) and support vector machine (SVM) methods. By combining two types of fragment-based descriptors, an optimal SVM model with an RBF kernel was obtained. The sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) were 86.54%, 83.39%, 84.76%, and 0.6946 for a training set (n=5,387), and 83.19%, 78.82%, 80.72%, and 0.6152 for a test set (n=5,383), respectively. The optimal SVM model was further validated by an independent dataset (n=1,470) with an overall accuracy of 82.38%. The results showed that these two types of fragment-based descriptors are, in some degree, complementary to each other, and can be combined to enhance model predictive power. In comparison with other 2-D or 3-D description methods, the combination of fragment descriptors seems extremely useful in constructing high-throughput screening models of CYP inhibitors in the process of drug discovery.