Development of Machine Learning and Chemical Language Model‐Based QSAR Models for Predicting Drug Residue Depletion Half‐Lives in Plasma and Tissues of Cattle Across Various Administration Routes
Zhicheng Zhang, Lisa A. Tell, Zhoumeng Lin · Journal of Veterinary Pharmacology and Therapeutics · 2025
ABSTRACT Accurate prediction of drug depletion half‐lives plays a pivotal role in determining extralabel withdrawal intervals and ensuring the safety of food products derived from livestock. In this study, we employed machine learning (ML)‐based quantitative structure–activity relationship (QSAR) models and an innovative chemical language model‐based QSAR approach (ImprovedChemBERTa) to estimate plasma and tissue half‐lives of drugs administered to cattle through different administration routes. Utilizing a dataset from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database, we developed one “descriptor‐free” ImprovedChemBERTa model and 20 ML‐QSAR models, integrating four different ML algorithms with five categories of molecular descriptors. Among ML‐QSAR approaches, the deep neural network (DNN) method employing all descriptors achieved the highest predictive accuracy (test R 2 : 0.37). In contrast, the ImprovedChemBERTa model significantly outperformed traditional methods, reaching a test R 2 of 0.69, underscoring the superior capability and transfer learning potential of chemical language models. Our findings highlight the effectiveness of chemical language model‐based QSAR strategies, which directly process raw chemical representations without requiring explicitly generated molecular descriptors. Overall, this work provides a robust foundation for advancing tissue‐specific QSAR modeling in major food‐animal species and supports global efforts toward enhanced food safety regulation.