We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database of compound- target associations augmented with molecular ontological labels. It also contains two computational tools for prediction of new associations. We describe two drug-repurposing systems: one, Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based on an information retrieval strategy, and another, based on non-negative matrix factorization together with compound similarity, that was inspired by recommender systems. We report the performance of both tools on a drug-repurposing task.
@article{arxiv.1807.09754,
title = {Data Infrastructure and Approaches for Ontology-Based Drug Repurposing},
author = {Stephen Boyer and Thomas Griffin and Sarath Swaminathan and Kenneth L. Clarkson and Dmitry Zubarev},
journal= {arXiv preprint arXiv:1807.09754},
year = {2018}
}