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

• URL: http://arxiv.org/abs/2105.10489v1
• Date: Fri, 7 May 2021 18:39:25 GMT
• Title: Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
• Authors: Jenna Bilbrey, Logan Ward, Sutanay Choudhury, Neeraj Kumar, Ganesh Sivaraman
• Abstract summary: We look at the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. We use an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity.
• Score: 11.853524110656991
• License: http://creativecommons.org/licenses/by/4.0/
• 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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