TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment

2026-06-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

TSAssistant is a multi-agent framework designed to automate and enhance Target Safety Assessment (TSA) report drafting, a critical process in drug discovery that integrates diverse evidence to evaluate therapeutic target safety liabilities. This system addresses the scalability and reproducibility challenges of traditional, manual TSA by employing a modular, section-based, human-in-the-loop paradigm. It utilizes specialized subagents to retrieve structured and unstructured data, alongside literature, from curated biomedical sources via standardized tool interfaces, generating individually citable, evidence-grounded report sections. The framework features a hierarchical instruction architecture and an interactive refinement loop, allowing toxicologists to manually edit, append, or re-invoke agents for specific sections while maintaining conversational memory. TSAssistant aims to reduce the mechanical burden of evidence synthesis, augmenting expert workflows while preserving human decision authority, and is being evaluated on over 50 therapeutic targets.

Key takeaway

For toxicologists and pharmaceutical scientists tasked with Target Safety Assessment, TSAssistant offers a structured approach to de-risk drug programs by automating evidence synthesis. You can leverage its multi-agent framework to generate traceable, evidence-grounded reports, significantly reducing manual drafting time. The human-in-the-loop design ensures you retain final decision authority and can refine sections iteratively, improving consistency and compliance while focusing your expertise on critical safety judgments.

Key insights

TSAssistant augments Target Safety Assessment with a human-in-the-loop multi-agent framework for structured, traceable evidence synthesis.

Principles

• Decouple agent logic, domain skills, and user intent.
• Isolate agent contexts to prevent error propagation.
• Embed human validation at modular stages.

Method

The system decomposes report generation into specialized subagents, governed by a three-layer instruction hierarchy, with programmatic enforcement and an interactive human-in-the-loop refinement loop.

In practice

• Automate evidence retrieval for TSA reports.
• Facilitate section-level review and revision.
• Integrate diverse biomedical data sources.

Topics

• Target Safety Assessment
• Multi-Agent Systems
• Human-in-the-Loop AI
• Drug Discovery
• Large Language Models
• Retrieval-Augmented Generation

Best for: AI Scientist, AI Engineer, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.