The AOPOntology: A Semantic Artificial Intelligence Tool for Predictive Toxicology

Abstract Toxicology needs artificial intelligence tools that can automate the prediction of toxicity. Today we are at an interesting nexus. We have thousands of chemicals in the environment that lack regulatory thresholds for determining risk. New high-throughput in vitro testing methods are becoming available to test these chemicals. Causal Adverse Outcome Pathway Networks (CAOPNs) are emerging that will allow us to make predictions based on perturbations of specific key events within the network. The AOPOntology was developed as infrastructure for this nexus, providing the ability to model and marry data from the in vitro tests for the thousands of chemicals and place them within the CAOPN framework to facilitate adverse outcome predictions. The AOPN is a functional specialized ontology that creates classes that model biological pathways and CAOPNs. Adverse outcome predictions are based on mathematical determinations of key events that are sufficient to infer adverse outcomes will occur, or biological i...