Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

TL;DR

This paper introduces GCGVAE, a novel genetic constrained graph variational autoencoder, for generating potential drugs against COVID-19 by leveraging viral protein structures and optimization algorithms, showing promising results.

Contribution

The paper presents a new GCGVAE model that effectively generates antiviral drug candidates for SARS-CoV-2 using virus protein data and genetic optimization techniques.

Findings

Generated molecules outperform existing drugs in effectiveness scores.

Model successfully applied to other viruses beyond SARS-CoV-2.

Optimization algorithms improve drug candidate quality.

Abstract

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.