Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

Area of Science:

• Biomedical Informatics
• Drug Discovery
• Computational Biology

Background:

• The COVID-19 pandemic necessitated rapid identification of effective therapeutics.
• Drug repurposing offers a time-efficient strategy for developing new treatments.
• Existing drugs for other conditions may hold potential against SARS-CoV-2.

Purpose of the Study:

• To identify and prioritize small molecules for COVID-19 drug repurposing.
• To leverage a comprehensive biomedical knowledge graph for drug discovery.
• To accelerate the search for novel therapeutic agents against COVID-19.

Main Methods:

• Construction of the CAS Biomedical Knowledge Graph.
• Identification of 1350 small molecules targeting host proteins and COVID-19 processes.
• Development of a computer algorithm for drug-ranking and prioritization.
• Analysis of top-ranked molecules based on molecular functions.

Main Results:

• 1350 potentially repurposable small molecules were identified.
• A drug-ranking algorithm prioritized these molecules.
• The top 50 molecules included 11 drugs already in clinical trials for COVID-19.
• New drug candidates with potential for clinical investigation were identified.

Conclusions:

• The CAS Biomedical Knowledge Graph effectively supports drug repurposing for COVID-19.
• Computational methods can accelerate the identification of promising therapeutic candidates.
• This approach can streamline drug discovery for COVID-19 and other diseases.