Background: In drug metabolism reactions, it has become increasingly important to
measure Michaelis constants (Km), which are used for a variety of purposes, including identification
of enzymes involved in drug metabolism, prediction of drug-drug interactions, etc. Cytochrome
P450s (CYPs) comprise a super family of major human enzymes responsible for drug
metabolism. Hence, computational prediction of Km in CYP-mediated reactions facilitates drug
development in an efficient and economical way.
Methods: In this study, we firstly constructed a large dataset of ten CYP isoforms associated with
169 binding substrates, and 210 experimental Km values in CYP-mediated reactions. To predict
Km of substrates metabolized by various CYP isoforms, we developed a general prediction model
by using resilient back-propagation neutral network algorithm, based on the structural and physicochemical
properties of the substrates and the metabolic specificity of the enzymes.
Results: The predictive Km values achieve a squared cross-validation correlation coefficients (Q2) of 0.73 with the
experimental values, which is better than that of the existing models. Moreover, our model can predict Km values of the
compounds metabolized by a wide range of CYP isoforms.
Conclusion: This tool will be useful in large-scale drug screening studies for CYP enzymes and helpful in the drug design