Motivation Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions have been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. Results We propose TransformerGO, a model that is capable of capturing the semantic similarity between gene ontology sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine learning-based approaches on large datasets from S. cerevisiae and H. sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. Availability and implementation https://github.com/Ieremie/TransformerGO Supplementary information Supplementary data are available at Bioinformatics online.