CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov–Arnold network

Antihypertensive peptides (AHTPs) are short-chain peptides derived from food or bioproteins through enzymatic hydrolysis, fermentation, or chemical synthesis, and they have demonstrated promising blood pressure-lowering effects. These peptides primarily function by inhibiting angiotensin-converting enzyme (ACE), regulating the renin-angiotensin system (RAS), and enhancing nitric oxide (NO) production in endothelial cells. Owing to their high bioactivity and safety, AHTPs have attracted considerable attention in both functional food and pharmaceutical research. In this study, we propose CKAN-ATHP, a predictive framework that integrates three pre-trained encoders-DistilProtBert, PubChem10M, and Prot-T5-to effectively represent peptide sequences. To mitigate data imbalance and enhance model generalization, a sample augmentation strategy is introduced in the feature space. Subsequently, a Convolutional Kolmogorov-Arnold Network (CKAN) is constructed and optimized using a hybrid loss function combining label smoothing with Kullback-Leibler divergence and cross-entropy loss. Benchmark experiments demonstrate that CKAN-ATHP consistently outperforms seven state-of-the-art methods under class-imbalanced conditions, achieving improvements of 12-24% in MCC and 5-15% in BACC, while maintaining competitive ACC and AUC. Case studies and kernel density estimation analyses further validate the robustness and discriminative capability of the proposed framework. All data and code are available at https://github.com/Njq0104/CKAN-ATHP. SCIENTIFIC CONTRIBUTION: CKAN-ATHP integrates protein language model embeddings with chemical molecular descriptors and employs a CKAN to capture nonlinear sequence features under class imbalance, enabling effi cient and robust antihypertensive peptide prediction while showing residues 4-7 as critical for peptide recognition.