  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.
