TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of
  Tools
Authors: name: Shanghua Gao, name: Richard Zhu, name: Zhenglun Kong, name: Ayush Noori, name: Xiaorui Su, name: Curtis Ginder, name: Theodoros Tsiligkaridis, name: Marinka Zitnik

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.