Residual Graph Convolutional Network for Protein Structure Understanding with Self-Derived Spatial Features

ResidualGNNPlus is a residue-level Graph Neural Network that reconstructs 39 structural and biochemical features-such as hydrophobicity, SASA, polarity, Secondary Structure data, and B-factors-directly from 3D protein structures. Leveraging residual GCN blocks and Layer Normalization, it ensures stable training and strong generalization, enabling efficient, automated protein structure analysis with high accuracy. This approach provides an automated, learning-based framework for protein structure analysis, offering both efficiency and precision in characterizing residue-level properties.