Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding

Abstract

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph–Based Approach

“…The interpretability of prediction results is an important indicator determining the practicality of the machine learning–based model [ 48 ]. Although current KG embedding methods can be used to directly predict outcomes, interpretable reasoning is a major challenge owing to the lack of intermediate knowledge in the prediction process [ 49 ]. The quantity and quality of the knowledge source can be considered as important factors in assessing the credibility of knowledge in the biomedical field [ 21 ].…”

Drug Repurposing Using Knowledge Graph Embeddings with a Focus on Vector-Borne Diseases: A Model Comparison

“…Lastly, developing additional evaluation criteria (e.g., molecular analysis [27]) could further increase the success rate of drug-repurposed candidates in laboratory validation.…”