  Capsule Networks have great potential to tackle problems in structural
biology because of their attention to hierarchical relationships. This paper
describes the implementation and application of a Capsule Network architecture
to the classification of RAS protein family structures on GPU-based
computational resources. The proposed Capsule Network trained on 2D and 3D
structural encodings can successfully classify HRAS and KRAS structures. The
Capsule Network can also classify a protein-based dataset derived from a
PSI-BLAST search on sequences of KRAS and HRAS mutations. Our results show an
accuracy improvement compared to traditional convolutional networks, while
improving interpretability through visualization of activation vectors.
