AI RESEARCH
TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment
arXiv CS.CL
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ArXi:2604.23938v1 Announce Type: new Target Safety Assessment (TSA) requires systematic integration of heterogeneous evidence, including genetic, transcriptomic, target homology, pharmacological, and clinical data, to evaluate potential safety liabilities of therapeutic targets. This process is inherently iterative and expert-driven, posing challenges in scalability and reproducibility. We present TSAssistant, a multi-agent framework designed to TSA report drafting through a modular, section-based, and human-in-the-loop paradigm.