@inproceedings{zheng-etal-2026-tsassistant,
title = "{TSA}ssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment",
author = "Zheng, Xiaochen and
Jiang, Zhiwen and
Tokar, David and
Cheng, Yexiang and
Serra, Alvaro and
Guerard, Melanie and
Hatje, Klas and
Doktorova, Tatyana",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.26/",
pages = "412--434",
ISBN = "979-8-89176-460-6",
abstract = "Target Safety Assessment (TSA) requires systematic integration of genetic, transcriptomic, target homology, pharmacological, and clinical data to evaluate potential safety liabilities of therapeutic targets. This process is labour-intensive and expert-dependent, posing challenges in scalability and reproducibility. We present TSAssistant, a human-in-the-loop multi-agent framework that decomposes TSA report generation into a workflow of specialised subagents, each responsible for a single report section. Each subagent retrieves evidence from curated biomedical sources through standardised tool interfaces and produces individually citable, evidence-grounded sections, with behaviour shaped by a hierarchical instruction architecture that separates coordination logic from domain expertise and user intent. To complement these soft constraints, programmatic execution hooks and persistent memory stores enforce hard constraints across the workflow, while an interactive refinement loop allows experts to review and revise individual sections with full conversational context preserved across iterations."
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<abstract>Target Safety Assessment (TSA) requires systematic integration of genetic, transcriptomic, target homology, pharmacological, and clinical data to evaluate potential safety liabilities of therapeutic targets. This process is labour-intensive and expert-dependent, posing challenges in scalability and reproducibility. We present TSAssistant, a human-in-the-loop multi-agent framework that decomposes TSA report generation into a workflow of specialised subagents, each responsible for a single report section. Each subagent retrieves evidence from curated biomedical sources through standardised tool interfaces and produces individually citable, evidence-grounded sections, with behaviour shaped by a hierarchical instruction architecture that separates coordination logic from domain expertise and user intent. To complement these soft constraints, programmatic execution hooks and persistent memory stores enforce hard constraints across the workflow, while an interactive refinement loop allows experts to review and revise individual sections with full conversational context preserved across iterations.</abstract>
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%0 Conference Proceedings
%T TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment
%A Zheng, Xiaochen
%A Jiang, Zhiwen
%A Tokar, David
%A Cheng, Yexiang
%A Serra, Alvaro
%A Guerard, Melanie
%A Hatje, Klas
%A Doktorova, Tatyana
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F zheng-etal-2026-tsassistant
%X Target Safety Assessment (TSA) requires systematic integration of genetic, transcriptomic, target homology, pharmacological, and clinical data to evaluate potential safety liabilities of therapeutic targets. This process is labour-intensive and expert-dependent, posing challenges in scalability and reproducibility. We present TSAssistant, a human-in-the-loop multi-agent framework that decomposes TSA report generation into a workflow of specialised subagents, each responsible for a single report section. Each subagent retrieves evidence from curated biomedical sources through standardised tool interfaces and produces individually citable, evidence-grounded sections, with behaviour shaped by a hierarchical instruction architecture that separates coordination logic from domain expertise and user intent. To complement these soft constraints, programmatic execution hooks and persistent memory stores enforce hard constraints across the workflow, while an interactive refinement loop allows experts to review and revise individual sections with full conversational context preserved across iterations.
%U https://aclanthology.org/2026.emnlp-industry.26/
%P 412-434
Markdown (Informal)
[TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment](https://aclanthology.org/2026.emnlp-industry.26/) (Zheng et al., EMNLP 2026)
ACL
- Xiaochen Zheng, Zhiwen Jiang, David Tokar, Yexiang Cheng, Alvaro Serra, Melanie Guerard, Klas Hatje, and Tatyana Doktorova. 2026. TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 412–434, Budapest, Hungary. Association for Computational Linguistics.