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arXiv · 2105.10489

Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates

Jenna Bilbrey·Logan Ward·Sutanay Choudhury·Neeraj Kumar·Ganesh Sivaraman
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Abstract

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.

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Jenna Bilbrey, Logan Ward, Sutanay Choudhury, Neeraj Kumar, Ganesh Sivaraman. 2021-05-07. Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates. https://arxiv.org/abs/2105.10489

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