Abstract

Welcome to the next level of network pharmacology!
HerbAgent is an AI-driven framework that leverages Large Language Models (LLMs) and multi-agent collaboration to automate and accelerate Traditional Chinese Medicine (TCM) network pharmacology research. By streamlining complex analytical workflows, HerbAgent provides an efficient and scalable solution for herbal medicine research and drug discovery.

Methodology

HerbAgent is a modular framework built to automate and streamline network pharmacology studies, particularly in the field of traditional Chinese medicine. It harnesses large language models (LLMs) within a multi-agent architecture for flexibility, scalability, and robustness. We employed Langchain as the agent orchestration framework and integrated Qwen-Max through API calls as the core LLM, though researchers can readily substitute other models like GPT-4 based on their preferences and requirements. Together, these components provide the robust natural language capabilities essential for handling network pharmacology analyses. The HerbAgent framework is composed of several key modules, each targeting a distinct component of the workflow, from data extraction and target prediction to network construction and result interpretation.

Results

🚀 Automated Workflow: Reduces manual effort by automating data retrieval, processing, and analysis.

🧠 AI-Powered Multi-Agent System: Modular agents specialized in pharmacological target identification, syndrome-disease mapping, and network analysis.

📊 Advanced Analytical Capabilities: Integrates many pharmacological databases and tools like protein-protein interaction (PPI) analysis and random walk with restart (RWR) algorithms.

💡 User-Friendly Interface: Interactive workflows for seamless communication and report generation.

🔍 Scalable & Flexible: Easily adaptable to other traditional and modern medicine systems.