Paula Andrea Marin Zapata

The paper validates a generative AI model using experimental assays to design small molecule drugs targeting specific cancer-related phenotypes, showing 88% morphological impact and 37% transcriptomic pathway effects.

This study uses self-supervised learning to improve image representation for Cell Painting, outperforming traditional tools in classification tasks and reducing computational costs.

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.