AI and Automation for Predictive Toxicology Using Cell Painting

Understanding and predicting chemical toxicity remains a major challenge in environmental safety and drug development. Traditional assays are often low-throughput, costly, and limited in their ability to capture complex biological responses. We present an integrated platform that combines cell painting, robotized lab automation, and artificial intelligence (AI) to transform toxicological profiling into a scalable and data-rich process. Using high-content imaging, our system generates detailed morphological fingerprints from cells exposed to diverse compounds. Automated instrumentation and imaging pipelines ensure high quality reproducible data and throughput. AI models trained on these data can predict mechanisms of toxicity and accurately classify compounds, as well as offer insights on single cell level. This integration of phenomics, automation, and AI provides a powerful framework for mechanistic insight, chemical safety assessment, and regulatory innovation.