Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity.

The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure-activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity. The results demonstrate high predictive performance across multiple targets, with balanced accuracy exceeding 0.80 for the majority of models. Furthermore, stability checks confirmed the consistency of predictive performance across multiple training-test splits. The results obtained by using QSAR predictions to identify known markers of adversities highlighted the utility of the models for risk assessment and for prioritizing compounds for further experimental evaluation.Scientific contributionThe work describes the development of QSAR models as tools for screening chemicals with potential systemic toxicity, thus contributing to resource savings and providing indications for further better-targeted testing. This study provides advances in the field of computational modeling of MIEs and information from AOP which is still relatively young and unexplored. The comprehensive modeling procedure is highly generalizable, and offers a robust framework for predicting a wide range of toxicological endpoints.