AOP-Wiki EMOD 3.0: AI-Driven Data Model for AOP-NAM Integration

A new paper on arXiv (2605.21645) introduces AOP-Wiki EMOD 3.0, an expanded data model and content evaluation framework that leverages agentic AI to improve integration between Adverse Outcome Pathways (AOPs) and New Approach Methodologies (NAMs). AOPs are logic models causally linking biological mechanisms to adverse outcomes for chemical regulatory endpoints, while NAMs are in vitro and in silico alternatives to animal testing. The AOP-Wiki, the global AOP repository, has faced constraints in its data model and infrastructure that limit growth. The authors propose using AI to aggregate and structure AOP-relevant information, harnessing core AOP principles to guide AI applications. The work aims to modernize the AOP-Wiki and support continued evolution of AOP frameworks.