Integrating chemical structure and high-throughput transcriptomics for mechanistically interpretable Tox21 bioactivity prediction

Predictive toxicology increasingly emphasizes methods that combine scalable chemical screening with biologically interpretable mechanistic information. Existing computational approaches, however, rely largely on chemical structure alone and often fail to capture the cellular programs underlying compound-induced cellular responses. Herein, we describe a multimodal modeling framework that integrates chemical fingerprints with high-throughput transcriptomic (HTTr) dose-response profiles to predict activity for 41 curated Tox21 assay endpoints. HTTr data were obtained from TempO-Seq screens in MCF-7, U-2 OS, and HepaRG cells following exposure to ToxCast compounds across an eight-point concentration series ranging from 0.03 to 100 µM. Using gradient-boosted decision trees and nested compound-aware cross-validation, 13 assays achieved robust performance (mean area under the precision-recall curve (AUPRC) > 0.75), spanning nuclear receptor signaling, stress-response pathways, and xenobiotic metabolism. SHapley Additive exPlanations (SHAP)-based feature attribution analysis showed that predictions depend on both structural motifs and transcriptional programs, in a manner consistent with established mechanistic relationships between chemical structure, nuclear receptor biology, and adaptive cellular responses. These findings illustrate how structure and high-throughput transcriptomic dose-response signature integration enables models that are accurate and mechanistically grounded, shifting computational toxicology toward transparent and biologically informed mechanistic bioactivity prediction.