Systemic Toxicity:  Informing Toxicologically Relevant Molecular and Cellular Targets Using Expert-Guided AI / Machine Learning (ML) Approach

This pilot project aims to develop a workflow for creating a comprehensive database of putative toxicological targets and their links to systemic toxicity mechanisms. This will be achieved through the following stages:

1. Data Extraction Pilot and Computational Method Validation: Develop AI models to extract toxicological data (targets, mechanisms) from scientific literature and databases; Establish rigorous validation criteria to ensure the accuracy and reliability of extracted information.
2. Data Inventory and Landscape Evaluation: Systematically assess the types and quality of data available from public sources (AOPWiki, CTDBase, etc.); Create a comprehensive summary of data integration possibilities and potential challenges.
3. Expert-Based Review: Engage domain experts in toxicology to review the extracted data and the overall workflow; Refine the process based on expert feedback to maximize scientific validity and utility.
4. Post-Pilot Planning: Develop a scalable workflow for expanding data extraction to a wider range of chemicals; Design strategies to integrate data with mechanistic data sources for a holistic view of systemic tox pathways.