MetaStab‐Analyzer: Classification and Regression Models for Metabolic Stability Prediction

The pharmacokinetic profile of a potential drug is largely determined by its metabolic stability, which reflects its susceptibility to biotransformation. Metabolic stability data allow one to assess the therapeutic value of a compound and its toxicological risk. This assesment relies primarily on pharmacokinetic parameters, particularly half-life (t_1/2) and clearance (CL), which are typically determined using in vitro systems including hepatocytes and liver microsomal fractions. Using the publicly available ChEMBL v. 35 and PubChem databases, we collected over 8000 chemical compounds with experimental intrinsic CL and/or half-life data from liver microsome assays obtained in mice, rats, and humans. Different thresholds were applied to differentiate the stable and unstable molecules. The Naive Bayesian classifier with MNA (Multilevel Neighborhoods of Atoms) descriptors and Self-Consistent Extreme Classifier (SCEC) with QNA (Quantitative Neighborhoods of Atoms) descriptors were used for creating classification models. The accuracy (AUC) of most classification models exceeded 0.85. Self-Consistent Regression was used to create quantitative models. The coefficient of determination of the regression models varied from 0.35 (rat, t_1/2) to 0.7 (human, CL_int). These models were integrated into the freely available web application MetaStab-Analyzer, which provides a unique combination of qualitative (stable/unstable/moderate) and quantitative predictions for three species. A key feature of the application is the providing of numerical metrics for each prediction, which increases its interpretability. This combination of innovative algorithms (SCR and SCEC), dual qualitative-quantitative assessment, and a user-friendly interface is not available in any existing tool. MetaStab-Analyzer is freely available at https://www.way2drug.com/metastab/.