  Motivation: Exploring drug-protein interactions (DPIs) work as a pivotal step
in drug discovery. The fast expansion of available biological data enables
computational methods effectively assist in experimental methods. Among them,
deep learning methods extract features only from basic characteristics, such as
protein sequences, molecule structures. Others achieve significant improvement
by learning from not only sequences/molecules but the protein-protein and
drug-drug associations (PPAs and DDAs). The PPAs and DDAs are generally
obtained by using computational methods. However, existing computational
methods have some limitations, resulting in low-quality PPAs and DDAs that
hamper the prediction performance. Therefore, we hope to develop a novel
supervised learning method to learn the PPAs and DDAs effectively and thereby
improve the prediction performance of the specific task of DPI. Results: In
this research, we propose a novel deep learning framework, namely BridgeDPI.
BridgeDPI introduces a class of nodes named hyper-nodes, which bridge different
proteins/drugs to work as PPAs and DDAs. The hyper-nodes can be supervised
learned for the specific task of DPI since the whole process is an end-to-end
learning. Consequently, such a model would improve prediction performance of
DPI. In three real-world datasets, we further demonstrate that BridgeDPI
outperforms state-of-the-art methods. Moreover, ablation studies verify the
effectiveness of the hyper-nodes. Last, in an independent verification,
BridgeDPI explores the candidate bindings among COVID-19's proteins and various
antiviral drugs. And the predictive results accord with the statement of the
World Health Organization and Food and Drug Administration, showing the
validity and reliability of BridgeDPI.
