BiotaPredictor: A machine learning framework for predicting drug depletion by the human gut microbiota

The gut microbiota can profoundly influence the performance of oral medicines by metabolizing or bioaccumulating drugs, thereby reducing systemic exposure and contributing to variable patient responses. Despite growing recognition of these interactions, microbiota-driven drug depletion remains insufficiently addressed during early development. Here, we present BiotaPredictor, a freely accessible web-based machine learning platform that predicts the likelihood of drug depletion by human intestinal microbiota from molecular structure. We compiled two curated datasets containing microbial depletion outcomes: (i) a consistently annotated AstraZeneca dataset (158 compounds) and (ii) an expanded integrated dataset incorporating published evidence (522 compounds). Multiple molecular descriptors—including physicochemical properties, 3D conformational features, and fingerprint-based encodings—were evaluated across a suite of classification algorithms. A logistic regression model trained on the AstraZeneca dataset using physicochemical parameters achieved the highest performance, correctly classifying all compounds in an external validation study (3 depleted, 9 stable). BiotaPredictor enables early triage of compounds susceptible to microbiota-mediated loss and can inform lead optimization, formulation strategy, and pharmacokinetic prediction. Integrating microbiota liability assessment into development pipelines will contribute to safer, more efficacious drug products and support precision delivery of orally administered therapeutics.