biostudies-literatureCross-Zamirski JOBBSRC DTPAstraZeneca, SwedenAstraZenecaWellcome TrustBiotechnology and Biological Sciences Research CouncilEngineering and Physical Sciences Research Council10001https://www.ebi.ac.uk/biostudies/studies/S-EPMC9200748biostudies-literatureUnknown12(1)Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. Additionally, we identified 30 features whichScientific reportsLabel-free prediction of cell painting from brightfield images.PMC920074821-3688EP/N014588/1EP/S026045/1215733/Z/19/Z221633/Z/20/ZEP/T003553/1EP/T017961/1Turkki RCross-Zamirski JOMouchet ESchonlieb CBWilliams GWang YfalseLabel-free prediction of cell painting from brightfield images.Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. Additionally, we identified 30 features which2022-01-01T00:00:00Z2022 Jun2025-04-22T21:29:57.594Z2025-02-19T02:15:10.93ZS-EPMC92007483570559110.1038/s41598-022-12914-x