Confounder-aware foundation modeling for accurate phenotype profiling in cell imaging.

ABSTRACT: Image-based profiling is rapidly transforming drug discovery, offering unprecedented insights into cellular responses. However, experimental variability hinders accurate identification of mechanisms of action (MoA) and compound targets. Existing methods commonly fail to generalize to novel compounds, limiting their utility in exploring uncharted chemical space. To address this, we present a confounder-aware foundation model integrating a causal mechanism within a latent diffusion model, enabling the generation of balanced synthetic datasets for robust biological effect estimation. Trained on over 13 million Cell Painting images and 107 thousand compounds, our model learns robust cellular phenotype representations, mitigating confounder impact. We achieve state-of-the-art MoA and target pre

SUBMITTER: Papanastasiou G 

PROVIDER: S-EPMC12546604 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature