Abstract
The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine learning (ML), high-content assays like cell painting offer new opportunities for predictive toxicology. Cell painting captures extensive morphological features of cells, which can correlate with chemical bioactivity. In this study, we leveraged cell painting data to develop ML models for predicting mutagenicity and compared their performance with structure-based models. We used two datasets: a Broad Institute dataset containing profiles of over 30 000 molecules and a U.S.-Environmental Protection Agency dataset with images of 1200 chemicals tested at multiple concentrations. By integrating these datasets, we aimed to improve the robustness of our models. Among three algorithms tested-Random Forest, Support Vector Machine, and Extreme Gradient Boosting-the third showed the best performance for both datasets. Notably, selecting the most relevant concentration per compound, the phenotypic altering concentration, significantly improved prediction accuracy. Our models outperformed traditional quantitative structure activity relationship (QSAR) tools such as the Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) and the CompTox Dashboard for the majority of compounds, demonstrating the utility of cell painting features. The cell painting-based models revealed morphological changes related to DNA and RNA perturbation, especially in mitochondria, endoplasmic reticulum and nuclei, aligning with mutagenicity mechanisms. Despite this, certain compounds remained challenging to predict due to inherent dataset limitations and inter-laboratory variability in cell painting technology. The findings highlight the potential of cell painting in mutagenicity prediction, offering a complementary perspective to chemical structure-based models. Future work could involve harmonizing cell painting methodologies across datasets and exploring deep learning techniques to enhance predictive accuracy. Ultimately, integrating cell painting data with QSAR descriptors in hybrid models may unlock novel insights into chemical mutagenicity.