# Continuous Representation of Molecules Using Graph Variational Autoencoder

arXiv: https://arxiv.org/abs/2004.08152 · HTML: https://mlsanity.com/paper/2004.08152/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity](https://mlsanity.com/paper/2103.14536/) (2021)
- [Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation](https://mlsanity.com/paper/2201.01196/) (2022)
- [AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning](https://mlsanity.com/paper/2311.01118/) (2023)
- [Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction](https://mlsanity.com/paper/2411.13688/) (2024)
- [Relational VAE: A Continuous Latent Variable Model for Graph Structured Data](https://mlsanity.com/paper/2106.16049/) (2021)

## Abstract (arXiv)

In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is employed to prune the latent space and help the decoder in generating meaningful adjacency tensor of molecules. Other than the potential applicability in drug design and property prediction, we show the superior performance of this technique in comparison to other similar methods based on the SMILES representation of the molecules with RNN based encoder and decoder.