Computational prediction of mutagenicity through comprehensive cell painting analysis.

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 test

SUBMITTER: Cerisier N 

PROVIDER: S-EPMC12534217 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature