SELF-BART : A Transformer-Based Molecular Representation Model Using SELFIES

Abstract

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.

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Cite

Text

Priyadarsini et al. "SELF-BART : A Transformer-Based Molecular Representation Model Using SELFIES." NeurIPS 2024 Workshops: AI4Mat, 2024.

Markdown

[Priyadarsini et al. "SELF-BART : A Transformer-Based Molecular Representation Model Using SELFIES." NeurIPS 2024 Workshops: AI4Mat, 2024.](https://mlanthology.org/neuripsw/2024/priyadarsini2024neuripsw-selfbart/)

BibTeX

@inproceedings{priyadarsini2024neuripsw-selfbart,
  title     = {{SELF-BART : A Transformer-Based Molecular Representation Model Using SELFIES}},
  author    = {Priyadarsini, Indra and Takeda, Seiji and Hamada, Lisa and Brazil, Emilio Vital and Soares, Eduardo and Shinohara, Hajime},
  booktitle = {NeurIPS 2024 Workshops: AI4Mat},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/priyadarsini2024neuripsw-selfbart/}
}