TxAgent

Report Abuse

Basic Information

TxAgent is an AI agent designed to perform therapeutic reasoning by combining multi-step inference with a large toolbox of biomedical tools. It evaluates drug interactions at molecular, pharmacokinetic, and clinical levels, identifies contraindications given patient comorbidities and concurrent medications, and formulates personalized treatment strategies that account for age, genetic factors, and disease progression. The project integrates real-time biomedical knowledge retrieval from a consolidated ToolUniverse of 211 tools, which includes records for all US FDA-approved drugs since 1939 and validated clinical insights. TxAgent is provided as an installable Python package, includes pretrained model weights, and offers example scripts and a Gradio demo to run case studies and interactive sessions.

Links

App Details

Features
TxAgent features structured tool selection and function calls to execute complex therapeutic tasks, real-time retrieval and synthesis of evidence across multiple biomedical sources, and iterative refinement of recommendations through multi-step reasoning. The repository links to ToolUniverse, a 211-tool collection for drug and clinical analysis. It includes pretrained LLM weights and an embedding model, example scripts to run demonstrations and a Gradio app, and installation instructions for pip or source installation. Performance benchmarks are provided across five new datasets and quantitative results are reported, alongside hardware recommendations such as an H100 GPU with >80GB memory for running the full agent.
Use Cases
TxAgent helps clinicians, researchers, and developers by automating evidence-backed therapeutic reasoning and reducing manual synthesis of drug interaction and contraindication information. It produces personalized treatment suggestions that align with clinical guidelines and cross-source real-world evidence, which can lower the risk of adverse events and support safer prescribing decisions. The system generalizes across brand, generic, and descriptive drug references with low variance, demonstrates high accuracy on benchmark tasks, and exposes pretrained models and demo utilities to accelerate evaluation, prototyping, and integration into research workflows.

Please fill the required fields*