Meta^QM: Exploring the Role of QM Calculations in Drug Metabolism Prediction.

ABSTRACT: Understanding and predicting the metabolic fate of xenobiotics is essential in early drug discovery stages, as poor ADMET properties are a leading cause of new drug candidates' failure. In silico metabolism modeling offers a way to design safer and more effective compounds. We present Meta^QM, a set of random forest classifiers enhanced with quantum chemical descriptors to predict (i) the occurrence of metabolic reactions (Metaclass^QM) and (ii) the site of metabolism (Metaspot^QM). Models were trained on the MetaQSAR database, which contains 3788 expert-curated reactions divided into 3 main categories, 21 classes, and 101 subclasses. The descriptors used to train the models included physicochemical, constitutional, and stereo-electronic features computed at t

SUBMITTER: Macorano A 

PROVIDER: S-EPMC12732870 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature