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Streamline automated biomedical discoveries with agentic bioinformatics.

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Title:	Streamline automated biomedical discoveries with agentic bioinformatics.
Authors:	Zhou J; Syneron Opal, 10281, Cayman Island.; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence for Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence on Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, P.R. China., Jiang J; Department of Statistics, Nanjing University, Nanjing 210008, China., Han Z; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence for Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence on Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; School of Software, Shandong University, Jinan 250101, China., Wang Z; School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, P.R. China., Gao X; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence for Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.; Center of Excellence on Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Source:	Briefings in bioinformatics [Brief Bioinform] 2025 Aug 31; Vol. 26 (5).
Publication Type:	Journal Article; Review
Language:	English
Journal Info:	Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Imprint Name(s):	Publication: Oxford : Oxford University Press Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
MeSH Terms:	Computational Biology/methods , Artificial Intelligence , Biomedical Research*, Humans ; Precision Medicine ; Drug Discovery ; Synthetic Biology
Abstract:	The emergence of artificial intelligence agents powered by large language models marks a transformative shift in computational biology. In this new paradigm, autonomous, adaptive, and intelligent agents are deployed to tackle complex biological challenges, leading to a new research field named agentic bioinformatics. Here, we explore the core principles, evolving methodologies, and diverse applications of agentic bioinformatics. We examine how agentic bioinformatics systems work synergistically to facilitate data-driven decision-making and enable self-directed exploration of biological datasets. Furthermore, we highlight the integration of agentic frameworks in key areas such as personalized medicine, drug discovery, and synthetic biology, illustrating their potential to revolutionize healthcare and biotechnology. In addition, we address the ethical, technical, and scalability challenges associated with agentic bioinformatics, identifying key opportunities for future advancements. By emphasizing the importance of interdisciplinary collaboration and innovation, we envision agentic bioinformatics as a major force in overcoming the grand challenges of modern biology, ultimately advancing both research and clinical applications. (© The Author(s) 2025. Published by Oxford University Press.)
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Grant Information:	REI/1/5234-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); REI/1/5414-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); REI/1/5289-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); REI/1/5404-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); REI/1/5992-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); URF/1/4663-01-01 King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA); 5932 Center of Excellence for Smart Health; 5940 Center of Excellence on Generative AI; UDF01004172 Chinese University of Hong Kong, Shenzhen
Contributed Indexing:	Keywords: AI agents; bioinformatics; large language models
Entry Date(s):	Date Created: 20250928 Date Completed: 20250928 Latest Revision: 20251001
Update Code:	20251001
PubMed Central ID:	PMC12476841
DOI:	10.1093/bib/bbaf505
PMID:	41016012
Database:	MEDLINE

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Abstract:	The emergence of artificial intelligence agents powered by large language models marks a transformative shift in computational biology. In this new paradigm, autonomous, adaptive, and intelligent agents are deployed to tackle complex biological challenges, leading to a new research field named agentic bioinformatics. Here, we explore the core principles, evolving methodologies, and diverse applications of agentic bioinformatics. We examine how agentic bioinformatics systems work synergistically to facilitate data-driven decision-making and enable self-directed exploration of biological datasets. Furthermore, we highlight the integration of agentic frameworks in key areas such as personalized medicine, drug discovery, and synthetic biology, illustrating their potential to revolutionize healthcare and biotechnology. In addition, we address the ethical, technical, and scalability challenges associated with agentic bioinformatics, identifying key opportunities for future advancements. By emphasizing the importance of interdisciplinary collaboration and innovation, we envision agentic bioinformatics as a major force in overcoming the grand challenges of modern biology, ultimately advancing both research and clinical applications.<br /> (© The Author(s) 2025. Published by Oxford University Press.)
ISSN:	1477-4054
DOI:	10.1093/bib/bbaf505