# All SMILES Variational Autoencoder

arXiv: https://arxiv.org/abs/1905.13343 · HTML: https://mlsanity.com/paper/1905.13343/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 15 of 15)

- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Context-enriched molecule representations improve few-shot drug discovery](https://mlsanity.com/paper/2305.09481/) (2023)
- [Searching for High-Value Molecules Using Reinforcement Learning and Transformers](https://mlsanity.com/paper/2310.02902/) (2023)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Conditional β-VAE for De Novo Molecular Generation](https://mlsanity.com/paper/2205.01592/) (2022)
- [Latent Space Diffusion Models of Cryo-EM Structures](https://mlsanity.com/paper/2211.14169/) (2022)
- [High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling](https://mlsanity.com/paper/2305.02614/) (2023)
- [SALSA: Semantically-Aware Latent Space Autoencoder](https://mlsanity.com/paper/2310.02744/) (2023)
- [Improving VAE based molecular representations for compound property prediction](https://mlsanity.com/paper/2201.04929/) (2022)
- [Kernel-Elastic Autoencoder for Molecular Design](https://mlsanity.com/paper/2310.08685/) (2023)
- [Realistic molecule optimization on a learned graph manifold](https://mlsanity.com/paper/2106.13318/) (2021)
- [Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction](https://mlsanity.com/paper/2406.06553/) (2024)
- [Graph Residual based Method for Molecular Property Prediction](https://mlsanity.com/paper/2408.03342/) (2024)

## Abstract (arXiv)

Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions. To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, pooling hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation. By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior. Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.