Causality-integrated graph learning for multi-endpoint toxicity prediction.

Authors

Haoyue Tan, Tong Bao, Yin Fang, Rong Zhang, Jinsha Jin, Dan Xu, Lan Xie, Huan Zhong, Xuezhi Xiao, Huixiao Hong, Emilio Benfenati, Tadahaya Mizuno, Qing Zhou, Jingfan Qiu, Changsheng Qu, Yan Mao, Xiangyi Yu, Jing Guo, Hongxia Yu, Xiaowei Zhang, Wei Shi

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 41. Pages e2608919123. Oct 13, 2026. Epub Oct 05, 2026.

Abstract

Toxicity prediction remains a longstanding challenge in the safety assessment of numerous artificial chemicals. While conventional single-endpoint models are effective for predicting many molecular properties, they fail to capture the complex mechanisms underlying toxicity, which involve multiple molecular targets, interconnected pathways, and diverse outcomes. We present a causality-integrated graph learning framework that embeds toxicological mechanisms extracted from large-scale literature mining as directed graphs within deep learning (DL) models. By using chemical structure as input, the framework generates compound-specific perturbation profiles within a fixed causal space, enabling system-level classification, quantitative prioritization, and mechanistic interpretation across multiple outcomes. Focusing on endocrine-disrupting chemicals, we constructed a large-scale, causally organized knowledge graph (EDKG) and implemented the framework as EDKG-DL, a predictive model that incorporates mechanism-aware graph reasoning. Through extensive external validations, EDKG-DL outperforms structure-driven state-of-the-art approaches in both stability and cross-scenario generalization. This work establishes a mechanism-constrained, causality-informed learning paradigm that is highly relevant for multi-endpoint toxicity assessment and regulatory decision-making.

PMID:
42832660
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.