PolypharmDB: Quickly Identifying Repurposed Drug Candidates for COVID-19

Explore a cutting-edge approach to identifying potential COVID-19 treatments in this 29-minute talk by Stephen Mackinnon at the Toronto Machine Learning Series. Learn about PolypharmDB, a deep learning-based resource developed by Cyclica to accelerate drug discovery for the SARS-CoV-2 virus. Discover how the MatchMaker engine generates comprehensive drug-target predictions, mapping approximately 10,000 clinically tested drugs to around 8,000 human and 10 viral proteins. Gain insights into the collaborative efforts involving over 20 academic and industry partners working on computational strategies to define relevant target sets and experimental testing. Understand the importance of repurposing existing drugs with clinical data to expedite the process of finding therapeutic interventions for COVID-19, bypassing the time-consuming traditional drug discovery workflows.