Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models significantly enhance the efficiency of biomolecular analysis and synthesis, attracting widespread attention from academics and medicine. In this paper, we systematically investigate the application of prompt-based methods with LLMs to biological sequences, including DNA, RNA, proteins, and drug discovery tasks. Specifically, we focus on how prompt engineering enables LLMs to tackle domain-specific problems, such as promoter sequence prediction, protein structure modeling, and drug-target binding affinity prediction, often with limited labeled data. Furthermore, our discussion highlights the transformative potential of prompting in bioinformatics while addressing key challenges such as data scarcity, multimodal fusion, and computational resource limitations. Our aim is for this paper to function both as a foundational primer for newcomers and a catalyst for continued innovation within this dynamic field of study.
View on arXiv@article{jiang2025_2503.04135,
title={ Biological Sequence with Language Model Prompting: A Survey },
author={ Jiyue Jiang and Zikang Wang and Yuheng Shan and Heyan Chai and Jiayi Li and Zixian Ma and Xinrui Zhang and Yu Li },
journal={arXiv preprint arXiv:2503.04135},
year={ 2025 }
}