Data Infrastructure and Approaches for Ontology-Based Drug Repurposing

TL;DR

This paper presents a novel data infrastructure utilizing chemical ontologies and two computational tools for drug repurposing, demonstrating their effectiveness in predicting new drug-target associations.

Contribution

It introduces a new data infrastructure with ontological labels and two innovative computational tools for drug repurposing, combining information retrieval and matrix factorization methods.

Findings

Both tools show promising performance in drug-repurposing tasks.

The infrastructure effectively integrates chemical ontologies with compound-target data.

Recommender system-inspired approach enhances prediction accuracy.

Abstract

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.