TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools (2503.10970v1)

Abstract: Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TxAgent evaluates how drugs interact at molecular, pharmacokinetic, and clinical levels, identifies contraindications based on patient comorbidities and concurrent medications, and tailors treatment strategies to individual patient characteristics. It retrieves and synthesizes evidence from multiple biomedical sources, assesses interactions between drugs and patient conditions, and refines treatment recommendations through iterative reasoning. It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation. The ToolUniverse consolidates 211 tools from trusted sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets. TxAgent outperforms leading LLMs, tool-use models, and reasoning agents across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, covering 3,168 drug reasoning tasks and 456 personalized treatment scenarios. It achieves 92.1% accuracy in open-ended drug reasoning tasks, surpassing GPT-4o and outperforming DeepSeek-R1 (671B) in structured multi-step reasoning. TxAgent generalizes across drug name variants and descriptions. By integrating multi-step inference, real-time knowledge grounding, and tool-assisted decision-making, TxAgent ensures that treatment recommendations align with established clinical guidelines and real-world evidence, reducing the risk of adverse events and improving therapeutic decision-making.

• The paper introduces TxAgent, an AI agent using multi-step reasoning and a 211-tool ToolUniverse to perform therapeutic reasoning tasks with 92.1% accuracy on drug reasoning benchmarks.
• TxAgent leverages real-time biomedical knowledge, analyzes drug interactions and contraindications, and formulates patient-specific strategies across molecular, pharmacokinetic, and clinical data levels.
• TxAgent's design aims to align treatment recommendations with clinical guidelines and real-world evidence, potentially reducing adverse events and improving decision-making in healthcare.
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The paper "TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools" (2503.10970) introduces TxAgent, an AI agent designed for therapeutic reasoning, leveraging multi-step reasoning and real-time biomedical knowledge retrieval from a toolbox of 211 tools, called ToolUniverse. TxAgent analyzes drug interactions, contraindications, and formulates patient-specific treatment strategies, using data at molecular, pharmacokinetic, and clinical levels.

Core Functionality and Architecture

TxAgent's architecture allows it to evaluate drug interactions, identify contraindications based on patient comorbidities, and customize treatment strategies. It operates by retrieving and synthesizing evidence from biomedical sources, assessing interactions between drugs and patient conditions, and iteratively refining treatment recommendations. The agent selects tools based on task objectives and executes structured function calls to solve therapeutic tasks requiring clinical reasoning and cross-source validation. The ToolUniverse consolidates 211 tools from sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets.

Performance Benchmarking and Results

TxAgent was evaluated across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, encompassing 3,168 drug reasoning tasks and 456 personalized treatment scenarios. The agent achieved 92.1% accuracy in open-ended drug reasoning tasks, outperforming models like GPT-4o and DeepSeek-R1 (671B). TxAgent's performance remains consistent across drug name variations, maintaining a variance within $<$0.01. On the DescriptionPC benchmark, TxAgent achieved 56.5% accuracy, demonstrating its ability to deduce drug identities from descriptive narratives.

Implications and Future Directions

TxAgent's design facilitates the alignment of treatment recommendations with established clinical guidelines and real-world evidence, potentially reducing the risk of adverse events and improving therapeutic decision-making. Future development may focus on expanding the ToolUniverse and incorporating new biomedical insights. The iterative training approach for components like ToolRAG and the integration of varied data modalities (e.g., image or genomics data) could further enhance TxAgent's capabilities. Cloud-based deployments could broaden access to advanced treatment recommendations, while enhancing interpretability and decision trace transparency could establish new standards for explainability in AI-driven healthcare tools.