  In recent years, the scientific community has become increasingly interested
on peptides with non-canonical amino acids due to their superior stability and
resistance to proteolytic degradation. These peptides present promising
modifications to biological, pharmacological, and physiochemical attributes in
both endogenous and engineered peptides. Notwithstanding their considerable
advantages, the scientific community exhibits a conspicuous absence of an
effective pre-trained model adept at distilling feature representations from
such complex peptide sequences. We herein propose PepLand, a novel pre-training
architecture for representation and property analysis of peptides spanning both
canonical and non-canonical amino acids. In essence, PepLand leverages a
comprehensive multi-view heterogeneous graph neural network tailored to unveil
the subtle structural representations of peptides. Empirical validations
underscore PepLand's effectiveness across an array of peptide property
predictions, encompassing protein-protein interactions, permeability,
solubility, and synthesizability. The rigorous evaluation confirms PepLand's
unparalleled capability in capturing salient synthetic peptide features,
thereby laying a robust foundation for transformative advances in
peptide-centric research domains. We have made all the source code utilized in
this study publicly accessible via GitHub at
https://github.com/zhangruochi/pepland
