Drug-Target-Disease Embeddings Improve the Accuracy of Drug Repurposing Model

Drug repurposing offers a promising strategy to accelerate therapeutic development by identifying new indications for approved drugs. Recent work in this area has made extensive use of biomedical knowledge graphs (KGs). However, most computational models built on KGs predict drug-disease associations without explicitly incorporating information about molecular targets of drug molecules. We present a target-aware drug repurposing framework that leverages drug-target-disease (DTD’) triplet embeddings to explicitly capture target information. We generated 512-dimensional Node2Vec embeddings for DTD’ combinations labeled as either “positives” (fully connected DTD’ triplets in ROBOKOP KG) or “negatives” (random DTD’ combinations with no known DD’, i.e., “treats”, connections) and built multiple binary classification machine learning models. Multilayer Perceptron (MLP) models using DTD’ triplet embeddings outperformed matched drug-disease (DD’) doublet models across all classification and ranking metrics such as precision (0.61 vs 0.41, respectively), recall (0.76 vs 0.35), F1-score (0.68 vs 0.38) and enrichment factor (EF1%: 27.54 vs 15.56). These models also outperformed both XGBoost and Random Forest baselines, and Node2Vec embeddings yielded superior performance compared to embeddings from pretrained large language models such as PubMedBERT, and BioBERT. Our results confirm that explicit target information does improve the predictive accuracy of drug repurposing models built with KGs. We employed our models to generate novel repurposing hypotheses for approximately 22,000 human diseases annotated in the Mondo Disease Ontology. We present case studies of top-ranked drug repurposing candidates nominated by our approach for two neurodegenerative diseases (ALS and Huntington’s) supported by evidence in PubMed and ClinicalTrials.gov.