The rise and potential opportunities of large language model agents in bioinformatics and biomedicine
Tiantian Yang, Yihang Xiao, Zhijie Bao, Jianye Hao, Jiajie Peng
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AChecked 1 Oct 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.
| Source | License | Open-access status | Read as |
|---|---|---|---|
| openalex | cc-by-nc | gold | Yellow |
| crossref | https://creativecommons.org/licenses/by-nc/4.0/ | — | Yellow |
| unpaywall | cc-by-nc | gold | Yellow |
| europepmc | cc by-nc | — | Yellow |
Abstract
BLarge language model (LLM) agents have demonstrated remarkable potential in the fields of bioinformatics and biomedicine. This paper reviews the technical foundations of LLM agents, including their core architecture, key technologies, and collaborative modes. We explore the applications of LLM agents in multi-omics, drug development, chemical research, clinical diagnosis, and health management. The paper also analyzes the major challenges faced by LLM agents, such as the interaction and extension of their frameworks, data privacy and security, model hallucinations and interpretability, timeliness of knowledge updates, and ethical and legal risks. Furthermore, we discuss future directions, including paradigms for human-artificial intelligence collaboration and the development of open-source ecosystems and standardization. This paper aims to provide a comprehensive perspective and guidance on the advancement of LLM agents in bioinformatics and biomedicine.
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