  Design of new drug compounds with target properties is a key area of research
in generative modeling. We present a small drug molecule design pipeline based
on graph-generative models and a comparison study of two state-of-the-art graph
generative models for designing COVID-19 targeted drug candidates: 1) a
variational autoencoder-based approach (VAE) that uses prior knowledge of
molecules that have been shown to be effective for earlier coronavirus
treatments and 2) a deep Q-learning method (DQN) that generates optimized
molecules without any proximity constraints. We evaluate the novelty of the
automated molecule generation approaches by validating the candidate molecules
with drug-protein binding affinity models. The VAE method produced two novel
molecules with similar structures to the antiretroviral protease inhibitor
Indinavir that show potential binding affinity for the SARS-CoV-2 protein
target 3-chymotrypsin-like protease (3CL-protease).
