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Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration.

Keyan Ding1,2, Jing Yu2,3, Junjie Huang2

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.

Nature Computational Science
|August 20, 2025
PubMed
Summary
This summary is machine-generated.

SciToolAgent automates hundreds of scientific tools in biology, chemistry, and materials science using a knowledge graph. This large language model-powered agent makes complex research tools accessible to all scientists.

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Area of Science:

  • Computational biology
  • Computational chemistry
  • Materials science

Background:

  • Scientific research increasingly depends on specialized computational tools, demanding significant domain expertise.
  • Large language models (LLMs) show potential for tool automation but struggle with orchestrating multiple tools for complex workflows.

Purpose of the Study:

  • To introduce SciToolAgent, an LLM-powered agent designed to automate hundreds of scientific tools across diverse disciplines.
  • To enable intelligent tool selection and execution for complex scientific workflows.

Main Methods:

  • SciToolAgent utilizes a scientific tool knowledge graph for graph-based retrieval-augmented generation.
  • A comprehensive safety-checking module is integrated for responsible and ethical tool usage.

Main Results:

  • SciToolAgent successfully automates hundreds of scientific tools in biology, chemistry, and materials science.
  • Evaluations on a curated benchmark show SciToolAgent outperforms existing approaches.
  • Case studies demonstrate automation of complex workflows in protein engineering, chemical reactivity prediction, chemical synthesis, and MOF screening.

Conclusions:

  • SciToolAgent effectively automates complex scientific workflows, enhancing accessibility to advanced research tools.
  • The agent bridges the gap for both experts and non-experts in utilizing sophisticated computational resources.