Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.

ABSTRACT: Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning. SemiSupCon enhances downstream prediction performance of classifying MeSH pharmacological classifications from PubChem, as well as mode of action and biological target annotations from the Drug Repurposing Hub across two publicly available

SUBMITTER: Bushiri Pwesombo D 

PROVIDER: S-EPMC11776044 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature