Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).
Keywords: AUC, area under the ROC curve; Epoxidation; IAP, invariant accuracy of prediction; LMNA, labelled multilevel neighbourhoods of atom; LOO CV, leave-one-out cross-validation; MNA, multilevel neighbourhoods of atom; PASS; SOE, acute toxicity; SOM, site of metabolism; SoLA, structure with one labelled atom; biotransformation; metabolism; prediction; prediction of activity spectra for substances; reactive metabolite; tienilic acid; toxic metabolite; xenobiotics metabolism.