Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors

Abstract

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA− representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

“…The latest results in our previously published work support the S N 2 reaction mechanism . Only one model for predicting SoM for FMOs has been published by Fu and Lin, who used descriptors derived from quantum mechanics (e.g., Fukui reactivity indices) and circular fingerprints to train a support-vector machine classification model …”

“…30 Only one model for predicting SoM for FMOs has been published by Fu and Lin, who used descriptors derived from quantum mechanics (e.g., Fukui reactivity indices) and circular fingerprints to train a support-vector machine classification model. 6 Uridine 5′-Diphospho-glucuronosyltransferases. UGTs are considered the second most important enzymes for drug metabolism, after CYPs, and the most important enzymes of the conjugation phase.…”

“…Therefore, optimizing the structure of new chemical entities can be more cost-effective, and toxic metabolites can be identified early in the project. , Historically, predictive models have targeted the metabolism by human isoforms of the Cytochrome P450 (CYP) family of enzymes due to their irrefutable importance in the metabolism of drug-like compounds in the modification phase (Phase I) . However, studies on how to predict metabolism for other modification phase enzymes, such as aldehyde oxidases (AO) , and flavin-containing monooxygenases (FMO), and conjugation phase (Phase II) enzymes, such as uridine 5′-diphospho-glucuronosyltransferases (UGT), − are increasing in prevalence.…”