Foundation models (FMs) are able to leverage 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
of biomedical FMs, we present BioBridge, a novel parameter-efficient learning
framework, to bridge independently trained unimodal FMs to establish multimodal
behavior. BioBridge achieves it by utilizing Knowledge Graphs (KG) to learn
transformations between one unimodal FM and another without fine-tuning any
underlying unimodal FMs. Our empirical results demonstrate that BioBridge can
beat the best baseline KG embedding methods (on average by around 76.3%) in
cross-modal retrieval tasks. We also identify BioBridge demonstrates
out-of-domain generalization ability by extrapolating to unseen modalities or
relations. Additionally, we also show that BioBridge presents itself as a
general purpose retriever that can aid biomedical multimodal question answering
as well as enhance the guided generation of novel drugs