Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
This work addresses the urgent need for accelerated drug discovery in pandemics like COVID-19, though it is incremental as it applies existing models to a new target.
The authors tackled the problem of designing novel drug candidates targeting SARS-CoV-2 viral proteins using graph generative models, resulting in a framework that generates molecules balancing druglikeness, synthetic accessibility, and anti-SARS activity based on IC50 values.
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on \icfifty. This generative framework\footnote{https://github.com/exalearn/covid-drug-design} will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates.