Although it has been proposed for decades to predict site of metabolism (SOM) by in silico methods, identifying SOM correctly remains an unsolved fundamental problem and is an active area of research. In our prior works, we proposed a chemical bond-based approach to construction of SOM prediction models by integrating chemical bond descriptors and drug-metabolizing enzymes data. Although it has been evaluated with both 10-fold cross-validation and independent validation, we believe comparisons between this method and prior methods using publicly accessible external datasets are indispensable and more desirable. In the current study, based on chemical bond-based method, metabolism data released by Sheridan et al. and Zaretzki et al. was utilized to establish metabolite prediction models for CYP450 3A4, 2D6, and 2C9. Five major reaction types were involved, including Aliphatic C-hydroxylation, Aromatic C-hydroxylation, N-dealkylation, O-dealkylation, and S-Oxidation. Consequently, all our five models showed impressive performance on predicting SOMs, with accuracy and area under curve exceeded 0.940 and 0.953, respectively. Compared to prior works, our models were better than SOMP both in "SOM-scale" and "molecule-scale". In conclusion, comparisons between chemical-bond based method and prior works were conducted for the first time, which demonstrated that chemical-bond based method is better than or at least comparable to prior works.
Keywords: CYP450 2C9; CYP450 2D6; CYP450 3A4; Chemical bond-based method; Metabolites prediction; SOMP.
Copyright © 2018. Published by Elsevier Inc.