Self-supervision advances morphological profiling by unlocking powerful image representations.

Cell Painting is an image-based assay that offers valuable insights into drug mechanisms of action and off-target effects. However, traditional feature extraction tools such as CellProfiler are computationally intensive and require frequent parameter adjustments. Inspired by recent advances in AI, we trained self-supervised learning (SSL) models DINO, MAE, and SimCLR on a subset of the JUMP Cell Painting dataset to obtain powerful representations for Cell Painting images. We assessed these SSL features for reproducibility, biological relevance, predictive power, and transferability to novel tasks and datasets. Our best model (DINO) surpassed CellProfiler in drug target and gene family classification, significantly reducing computational time and costs. DINO showed remarkable generalizability without fine-tuning, outperforming CellProfiler on an unseen dataset of genetic perturbations. In bioactivity prediction, DINO achieved comparable performance to models trained directly on Cell Painting images, with only a small gap between supervised and self-supervised approaches. Our study demonstrates the effectiveness of SSL methods for morphological profiling, suggesting promising research directions for improving the analysis of related image modalities.