Line-walking method for predicting the inhibition of P450 drug metabolism

A new method, called line-walking recursive partitioning (LWRP), for partitioning diverse structures on the basis of chemical properties that uses only nine descriptors of the shape, polarizability, and charge of the molecule is described. We use a training set of over 600 compounds and a validation set of 100 compounds for the cytochrome P450 enzymes 2C9, 2D6, and 3A4. The LWRP algorithm itself incorporates elements from support vector machines (SVMs) and recursive partitioning, while circumventing the need for the linear or quadratic programming methods required in SVMs. We compare LWRP with a many-descriptor SVM model, using the same dataset as that described in the literature.(1) The line-walking method, using nine descriptors, predicted the validation set with about 84-90% accuracy, a success rate comparable to that of the SVM method. Furthermore, line-walking was able to find errors in the assignment of inhibitor values within the validation set for the 2C9 inhibitors. When these errors are corrected, the model predicts with an even higher level of accuracy. Although this method has been applied to P450 enzymes, it should be of general use in partitioning molecules on the basis of function.