Protein interface prediction using graph convolutional networks
Determining the interface between two interacting proteins can help illuminate cellular biology, improve our understanding of disease, and aid pharmaceutical research. Such determination is expensive and time consuming using wet-lab experiments, which has motivated the development of computational methods. Inspired by the success of deep learning in image processing and other application areas, we adapt convolutional neural networks to work with irregularly structured data, such as proteins. We construct a novel pairwise classification architecture which is trained and tested with data from the ...
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