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.
Zhou et al. (Sun,) studied this question.
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