With the disruptive nature of the COVID-19 pandemic, effective treatments could save the lives of severely ill patients, protect individuals with a high risk of infection, and reduce the time patients spend in hospital beds. However, there are currently no effective treatments for COVID-19. Traditional methodologies take years to develop and test compounds from scratch. Machine learning provides promising new approaches to repurpose drugs that are safe and already approved for other diseases. This project will develop a machine learning toolset to expedite the development of safe and effective medicines for COVID-19. The toolset will rapidly identify safe repurposing opportunities for approved and experimental drugs. It will predict whether treatments may have therapeutic effects in COVID-19 patients, allowing the identification of drugs and drug cocktails that are safe and plentiful enough to treat a substantial number of patients. By putting tools in the hand of practitioners, the activities in this project will have an immediate impact. They will result in actionable predictions that are accurate and interpretable.

Recently, the principal investigators have developed a series of machine learning tools to identify drug repurposing opportunities. Building on foundational previous work, in this project, the principal investigators will first build a large COVID-19 focused knowledge graph that will capture fundamental and COVID-19-specific biological knowledge. The graph learning methods will be adapted to identify safe drugs and drug cocktails for COVID-19. To predict the safety of cocktails with two or more drugs, the methods will generalize to an exponentially large space of high-order drug combinations. In addition to drug safety, efficacy is a crucial endpoint for drug development. The project will develop a novel graph neural network (GNN) method to identify efficacious drug repurposing opportunities, even for diseases, such as COVID-19, that do not yet have any drug treatments and thereby, no label, supervised information. The method will predict what drugs and drug combinations may have a therapeutic effect on COVID-19. Finally, the principal investigators will integrate the developed tools into a complete, explainable framework that will generate predictions, provide explanations, and incorporate human feedback into the machine learning loop. This project will provide new, open tools for rapid drug repurposing that will be relevant for COVID-19 and other emerging pathogens. Additionally, the project will provide unique opportunities for multi-disciplinary curriculum development, training and advising, and professional activities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Harvard University
United States
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