Affiliation:
1. Genomics and Computational Biology (GCB) Graduate Program University of Pennsylvania Philadelphia Pennsylvania USA
2. Institute for Biomedical Informatics University of Pennsylvania Philadelphia Pennsylvania USA
3. Center of Excellence in Environmental Toxicology University of Pennsylvania Philadelphia Pennsylvania USA
4. Department of Computational Biomedicine Cedars‐Sinai Medical Center Los Angeles California USA
Abstract
AbstractIn computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State‐of‐the‐art models are either limited by low accuracy, or lack of interpretability due to their black‐box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical‐gene connections, gene‐pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.
Subject
Pharmacology (medical),Modeling and Simulation
Cited by
1 articles.
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