O-QT assistant: a multi-agent AI system for streamlined chemical hazard assessment and read-across analysis using the OECD QSAR toolbox API

The OECD QSAR Toolbox is a vital resource in regulatory toxicology for assessing chemical hazards and filling data gaps using in silico methods, supporting the move away from animal testing. However, manually interpreting its complex outputs (physicochemical properties, profiling results, experimental data) and synthesizing this information into consistent, justified assessment reports represents a significant bottleneck requiring substantial expert effort. To address this challenge, we developed the O-QT assistant: the first open-source (Apache 2.0 licensed) pipeline employing a multi-agent Large Language Model (LLM) system, featuring distinct agents for interpreting properties, environmental fate, reactivity, metabolism, QSAR predictions, experimental data, and read-across strategies. The system offers both automated analysis and a guided mode allowing user customization of scope and methods. We demonstrate the O-QT Assistant’s workflow using 1,1-diethoxyheptane (CAS 688–82-4), a fragrance ingredient, as a detailed case study, supplemented by characterization across nine additional chemicals. Its LLM agents, operating under constraints derived from structured prompts and the retrieved data, synthesized these findings into a narrative report and a comprehensive JSON log. This approach, validated across multiple chemicals demonstrating high factual accuracy (>99 %), enables full auditability of the AI interpretations against the source data.. The O-QT Assistant is freely available on GitHub at https://github.com/VHP4Safety/O-QT-OECD-QSAR-Toolbox-AI-assistant under the Apache 2.0 license. By automating key interpretation and reporting steps, the O-QT Assistant has the potential to significantly improve the efficiency and consistency of workflows involving OECD QSAR Toolbox data, promoting more standardized interpretations and potentially reducing variability in chemical safety assessments. An open-source multi-agent LLM assistant automating OECD QSAR Toolbox data interpretation and narrative report generation via its API for regulatory toxicology workflows.