Artificial intelligence (AI) and machine learning are transforming toxicological research and chemical safety assessment. Although user-friendly computational toxicology platforms are increasingly available, integrating, customizing, and deploying AI methods within end-to-end workflows still often requires programming expertise. This barrier increases the time to adoption of new methods and slows regulatory uptake. To address this limitation, we survey recent initiatives democratizing computational toxicology through no-code/low-code pipelines, automated workflows, and open-source tools. We emphasize solutions for four computational needs: (i) data extraction and access, (ii) data mining and curation, (iii) data analysis and visualization, and (iv) modeling and prediction. These initiatives transform complex computational methods into guided and web-accessible applications that enable toxicologists, regulators, and researchers to leverage AI without coding expertise. The broad applicability of computational methods will be essential for supporting and scaling federal initiatives that advance human-relevant alternatives to animal testing. We also offer practical considerations for domain-specific tool development, including large language model-based information extraction, chemical structure standardization, interactive chemical grouping, and the development of validated machine learning models, as used in the Modeling and Visualization (MoVIZ) pipeline. The authors map the future of computational toxicology and cheminformatics, one that does not require scientists to become programmers but rather makes sophisticated AI tools more broadly accessible, transparent, and guided through thoughtful interface design, transparent workflows, and open science initiatives.
Mansouri et al. (Fri,) studied this question.