  With the advancements in Artificial intelligence (AI) and the accumulation of
healthrelated big data, it has become increasingly feasible and commonplace to
leverage machine learning technologies to analyze clinical and omics metadata
to assess the possibility of adverse drug reactions or events (ADRs) in the
course of drug discovery. Here, we have described a novel approach that
combined drug-induced gene expression profile from Open TG-GATEs
(Toxicogenomics Project-Genomics Assisted Toxicity Evaluation Systems) and ADR
occurrence information from FAERS (FDA [Food and Drug Administration] Adverse
Events Reporting System) database to predict the likelihood of ADRs. We
generated a total of 14 models using Deep Neural Networks (DNN) to predict
different ADRs; in the validation tests, our models achieved a mean accuracy of
85.71%, indicating that our approach successfully and consistently predicted
ADRs for a wide range of drugs. As an example, we have described the ADR model
in the context of Duodenal ulcer. We believe that our models will help predict
the likelihood of ADRs while testing novel pharmaceutical compounds, and will
be useful for researchers in drug discovery.
