ChemBioHepatox: Multimodal Integrating Chemical Structure and Biological Fingerprint for Robust and Interpretable Hepatotoxicity Prediction

Drug-induced liver injury (DILI) is a leading cause of
clinical
trial attrition and postmarketing withdrawal and a major contributor
to acute liver failure. As regulators increasingly encourage human-relevant,
nonanimal approaches, accurate and interpretable computational tools
for liver safety are needed. Existing hepatotoxicity models are limited
by activity cliffs, restricted applicability domains, accuracy, and
poor interpretability. We developed ChemBioHepatox, a multimodal framework
that couples chemical structure with biological assay responses to
improve predictive performance and mechanistic transparency. The framework
was pretrained on the DILIst data set (768 DILI-positive and 511 DILI-negative
compounds) and fine-tuned on a multisource downstream data set compiled
from seven studies. ChemBioHepatox achieved an AUC of 0.92 (precision
= 0.88, recall = 0.87), and 5-fold cross-validation under random,
scaffold-based, and cluster-based partitions further confirmed its
robustness and generalizability to unseen chemotypes. A linear classifier
operating on the concatenated structural embedding and 19 assay probabilities
enables direct attribution of each assay’s contribution via
its learned weights. External validations (including LiverTox severity
discrimination and targeted HepG2 CCK-8 assays) further confirmed
model-flagged high-risk hepatotoxicants among pesticides and food
additives. Activity-cliff analysis on the held-out test set further
revealed complementary contributions of structural embeddings and
mechanism-informed assay-response fingerprints. ChemBioHepatox advances
mechanism-driven liver safety assessment, supports regulatory modernization,
and provides openly available code, data, and a web interface (http://exposomex.cn:58080/).