Abstract
Adverse outcome pathways (AOP) structure toxicological knowledge as sequential, directed chains of key events (KE) that culminate in adverse outcomes (AOs). AOP development is a laborious process that involves extensive knowledge mining and could be improved via use of machine / deep learning. In this paper, we present an artificial intelligence system that can accelerate putative AOP development process by inferencing new AOP modules based on the knowledge learned from 16-million pre-parsed PubMed abstracts. Each AOP modules is represented as a triplet that consists of antecedent and consequent biological entities connected by a relation. Users can also investigate specific types of antecedent, consequent, and relations by specifying macro/microtemplates using the MeSH semantic type hierarchy. We also provide visualizations to illustrate the hidden semantics that our system can extract from input triplets.