  In the past several months, COVID-19 has spread over the globe and caused
severe damage to the people and the society. In the context of this severe
situation, an effective drug discovery method to generate potential drugs is
extremely meaningful. In this paper, we provide a methodology of discovering
potential drugs for the treatment of Severe Acute Respiratory Syndrome
Corona-Virus 2 (commonly known as SARS-CoV-2). We proposed a new model called
Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this
problem. We trained our model based on the data of various viruses' protein
structure, including that of the SARS, HIV, Hep3, and MERS, and used it to
generate possible drugs for SARS-CoV-2. Several optimization algorithms,
including valency masking and genetic algorithm, are deployed to fine tune our
model. According to the simulation, our generated molecules have great
effectiveness in inhibiting SARS-CoV-2. We quantitatively calculated the scores
of our generated molecules and compared it with the scores of existing drugs,
and the result shows our generated molecules scores much better than those
existing drugs. Moreover, our model can be also applied to generate effective
drugs for treating other viruses given their protein structure, which could be
used to generate drugs for future viruses.
