Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis

High-throughput screening and computational models have been effective in predicting chemical interactions with estrogen and androgen receptors, but similar approaches for steroidogenesis remain limited. To address this gap, we developed general steroidogenesis modulation models using data from ∼1,800 chemicals screened in H295R human adrenocortical carcinoma cells. A random forest model was validated using a prospective test set of 20 compounds (14 predicted active, 6 inactive), achieving 80% accuracy with conformal prediction adjustments. In parallel, we built classification and regression models based on IC₅₀ data from ChEMBL for key steroidogenic Enzymes, including CYP17A1, CYP21A2, CYP11B1, CYP11B2, 17β-HSD (1/2/3/5), 5α-reductase (1/2), and CYP19A1 (126-9,327 compounds per target). These models enable predictions of both general steroidogenesis inhibition and potential molecular targets. Additionally, we developed a transformer-based model (MolBART) to predict all end points simultaneously and validated this performance. Combined, these models may offer a rapid and scalable system for assessing chemical impacts on steroidogenesis, supporting chemical risk assessment, product stewardship, and regulatory decision-making.

MolBART; conformal predictors; endocrine disruption; large language models; machine learning; steroidogenesis.