# Decoding Molecular Graph Embeddings with Reinforcement Learning

arXiv: https://arxiv.org/abs/1904.08915 · HTML: https://mlsanity.com/paper/1904.08915/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design](https://mlsanity.com/paper/2510.21153/) (2025)
- [Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks](https://mlsanity.com/paper/2106.13277/) (2021)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Unsupervised Learning of Graph from Recipes](https://mlsanity.com/paper/2401.12088/) (2024)
- [Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling](https://mlsanity.com/paper/2410.11972/) (2024)
- [Auto-encoding Molecules: Graph-Matching Capabilities Matter](https://mlsanity.com/paper/2503.00426/) (2025)

## Abstract (arXiv)

We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated decoders that increase the complexity of training and evaluation (such as requiring parallel encoders and decoders or non-trivial graph matching). Here, we repurpose a simple graph generator to enable efficient decoding and generation of molecular graphs.