# SELF-BART : A Transformer-based Molecular Representation Model using SELFIES

arXiv: https://arxiv.org/abs/2410.12348 · HTML: https://mlsanity.com/paper/2410.12348/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 4 of 4)

- [A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools](https://mlsanity.com/paper/2506.20743/) (2025)
- [Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation](https://mlsanity.com/paper/2601.22757/) (2026)
- [How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?](https://mlsanity.com/paper/2601.06289/) (2026)
- [Does Pretraining Teach Chemical Language Models Chemistry?](https://mlsanity.com/paper/2610.18651/) (2026)

## Abstract (arXiv)

Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular structures. In this study, we develop an encoder-decoder model based on BART that is capable of leaning molecular representations and generate new molecules. Trained on SELFIES, a robust molecular string representation, our model outperforms existing baselines in downstream tasks, demonstrating its potential in efficient and effective molecular data analysis and manipulation.