# Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates

arXiv: https://arxiv.org/abs/2105.10489 · HTML: https://mlsanity.com/paper/2105.10489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 15 of 15)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [Multi-Objective De Novo Drug Design with Conditional Graph Generative Model](https://mlsanity.com/paper/1801.07299/) (2018)
- [NeVAE: A Deep Generative Model for Molecular Graphs](https://mlsanity.com/paper/1802.05283/) (2018)
- [SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules](https://mlsanity.com/paper/1703.07076/) (2017)
- [Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning](https://mlsanity.com/paper/2004.14308/) (2020)
- [Message-passing neural networks for high-throughput polymer screening](https://mlsanity.com/paper/1807.10363/) (2018)
- [CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models](https://mlsanity.com/paper/2004.01215/) (2020)
- [DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2004.08919/) (2020)
- [Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning](https://mlsanity.com/paper/2012.01736/) (2020)
- [PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models](https://mlsanity.com/paper/2005.13285/) (2020)
- [Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective](https://mlsanity.com/paper/2102.06045/) (2021)

## Abstract (arXiv)

We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on icfifty. This generative frameworkfootnotehttps://github.com/exalearn/covid-drug-design will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates.