Predicting Toxicity and Bioactivity of the Chemical Exposome: A Case Study for the Blood Exposome Database

Abstract Humans are exposed to thousands of chemicals throughout their life. Many of these chemicals are detected in blood and have been catalogued in the Blood Exposome Database. Comprehensive hazard assessment of a chemical requires time-consuming and costly lab experiments using animal or cell-lines, which cannot be easily scaled up to the chemical exposome, highlighting the urgent need for computational approaches that can prioritize chemicals based on toxicological information. In this study, we trained direct message passing neural networks (D-MPNN) models using the Chemprop framework chemical structure and bioactivity data from 9,458 compounds profiled in the U.S. EPA’s Tox21 program across 148 quantitative high-throughput screening assays. Additionally, we trained a complementary model using chemical structures (n=264,601) labeled with known UN GHS classifications for acute oral toxicity. Both models demonstrated strong predictive performance, with average AUCs exceeding 0.80 for 47 Tox21 assays. We applied these 48 models to 58,673 chemicals from the Blood Exposome Database to predict bioactivity and the GHS hazard classification, enabling scalable in-silico prioritization of understudied chemical exposures for further toxicological investigations. Data and code are available at https://zenodo.org/records/17560382 and https://github.com/idslme/exposome-toxicity-prediction .