Foundation models (FMs) learn from large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks. However, FMs developed for biomedical domains have largely remained unimodal, i.e., independently trained and used for tasks on protein sequences alone, small-molecule structures alone, or clinical data alone. To overcome this limitation, we present BioBRIDGE, a parameter-efficient
