# Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19

arXiv: https://arxiv.org/abs/2102.04977 · HTML: https://mlsanity.com/paper/2102.04977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [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)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [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)
- [PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models](https://mlsanity.com/paper/2005.13285/) (2020)

## Built on by (most cited first, 1 of 1)

- [Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design](https://mlsanity.com/paper/2310.14420/) (2023)

## Abstract (arXiv)

Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates: 1) a variational autoencoder-based approach (VAE) that uses prior knowledge of molecules that have been shown to be effective for earlier coronavirus treatments and 2) a deep Q-learning method (DQN) that generates optimized molecules without any proximity constraints. We evaluate the novelty of the automated molecule generation approaches by validating the candidate molecules with drug-protein binding affinity models. The VAE method produced two novel molecules with similar structures to the antiretroviral protease inhibitor Indinavir that show potential binding affinity for the SARS-CoV-2 protein target 3-chymotrypsin-like protease (3CL-protease).