In order to determine K(m) values of substrates for CYP3A4-mediated metabolism, an in silico model has been developed in the present work. Using electrotopological state (E-state) indices, together with Bayesian-regularized neural network (BRNN), we have described an in silico method to model log(1/K(m)) values of various substrates. The relative importance of the E-state indices is analyzed by principal component analysis. By using an additional external test set, which is independent of the training set, the robustness and predictivity of the model are also validated.
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