Design Proteins Using Large Language Models: Enhancements and Comparative Analyses
Proceedings of the 1st Workshop on Language + Molecules (L+M 2024), at ACL 2024, Bangkok, Thailand
Presented on , 9 citations on Google Scholar
In short
The paper retrains Mistral-7B, Llama-2-7B, Llama-3-8B and Gemma-7B on 42,000 human protein sequences to generate new protein sequences. Despite the small dataset, the models performed comparably to protein-specific models trained on millions of sequences, measured by pLDDT, RMSD, TM-score and REU.
Cite
@inproceedings{zeinalipour-etal-2024-design,
title = "Design Proteins Using Large Language Models: Enhancements and Comparative Analyses",
author = "Zeinalipour, Kamyar and
Jamshidi, Neda and
Bianchini, Monica and
Maggini, Marco and
Gori, Marco",
editor = "Edwards, Carl and
Wang, Qingyun and
Li, Manling and
Zhao, Lawrence and
Hope, Tom and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Language + Molecules ({L}+{M} 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.langmol-1.5/",
doi = "10.18653/v1/2024.langmol-1.5",
pages = "34--47"
}