  Recently, therapeutic peptides have demonstrated great promise for cancer
treatment. To explore powerful anticancer peptides, artificial intelligence
(AI)-based approaches have been developed to systematically screen potential
candidates. However, the lack of efficient featurization of peptides has become
a bottleneck for these machine-learning models. In this paper, we propose a
topology-enhanced machine learning model (Top-ML) for anticancer peptide
prediction. Our Top-ML employs peptide topological features derived from its
sequence "connection" information characterized by vector and spectral
descriptors. Our Top-ML model has been validated on two widely used AntiCP 2.0
benchmark datasets and has achieved state-of-the-art performance. Our results
highlight the potential of leveraging novel topology-based featurization to
accelerate the identification of anticancer peptides.
