Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
Siddhant Doshi, Sundeep Prabhakar Chepuri

TL;DR
This paper introduces Dr-COVID, a graph neural network model for drug repurposing that predicts potential treatments for COVID-19 by modeling complex biological interactions, achieving high ranking of known treatments.
Contribution
The paper presents a novel GNN-based model, Dr-COVID, for drug repurposing that is adaptable to any disease and leverages a heterogeneous graph to predict treatment links.
Findings
Predicted 150 potential COVID-19 drugs, 46 of which are in clinical trials.
Dr-COVID ranks actual treatment drugs within the top 15 for most diseases.
The model demonstrates effective prediction and ranking performance for drug repurposing.
Abstract
The 2019 novel coronavirus (SARS-CoV-2) pandemic has resulted in more than a million deaths, high morbidities, and economic distress worldwide. There is an urgent need to identify medications that would treat and prevent novel diseases like the 2019 coronavirus disease (COVID-19). Drug repurposing is a promising strategy to discover new medical indications of the existing approved drugs due to several advantages in terms of the costs, safety factors, and quick results compared to new drug design and discovery. In this work, we explore computational data-driven methods for drug repurposing and propose a dedicated graph neural network (GNN) based drug repurposing model, called Dr-COVID. Although we analyze the predicted drugs in detail for COVID-19, the model is generic and can be used for any novel diseases. We construct a four-layered heterogeneous graph to model the complex…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Computational Drug Discovery Methods · SARS-CoV-2 detection and testing
MethodsGraph Neural Network
