# Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

arXiv: https://arxiv.org/abs/2104.11674 · HTML: https://mlsanity.com/paper/2104.11674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [Tackling Over-pruning in Variational Autoencoders](https://mlsanity.com/paper/1706.03643/) (2017)
- [A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19](https://mlsanity.com/paper/2007.02202/) (2020)

## Abstract (arXiv)

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.