  With the fast development of COVID-19 into a global pandemic, scientists
around the globe are desperately searching for effective antiviral therapeutic
agents. Bridging systems biology and drug discovery, we propose a deep learning
framework for conditional de novo design of antiviral candidate drugs tailored
against given protein targets. First, we train a multimodal ligand--protein
binding affinity model on predicting affinities of antiviral compounds to
target proteins and couple this model with pharmacological toxicity predictors.
Exploiting this multi-objective as a reward function of a conditional molecular
generator (consisting of two VAEs), we showcase a framework that navigates the
chemical space toward regions with more antiviral molecules. Specifically, we
explore a challenging setting of generating ligands against unseen protein
targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related
target proteins. Using deep RL, it is demonstrated that in 35 out of 41 cases,
the generation is biased towards sampling more binding ligands, with an average
increase of 83% comparing to an unbiased VAE. We present a case-study on a
potential Envelope-protein inhibitor and perform a synthetic accessibility
assessment of the best generated molecules is performed that resembles a viable
roadmap towards a rapid in-vitro evaluation of potential SARS-CoV-2 inhibitors.
