iACP-KAN: Identifying Anticancer Peptides using Kolmogorov-Arnold Network

Anticancer peptides (ACPs) represent a promising therapeutic approach for cancer treatment, offering potential advantages over traditional methods by providing more selective targeting and reduced toxicity. However, the clinical implementation of ACPs has been hindered by insufficient effective prediction methods. This study introduces iACP-KAN, a novel computational model for identifying anticancer peptides using a Kolmogorov-Arnold Network (KAN) with innovative feature integration. The proposed methodology combines four handcrafted features: Dipeptide Composition, Binary Profile Features, Pseudo Amino Acid Composition, and K-mer Composition, with sequence embedding features learned through a Long Short-Term Memory. Utilizing two independent datasets, the model demonstrated superior performance across multiple evaluation metrics, consistently outperforming seven state-of-the-art methods. For Dataset 1, the proposed approach achieved the highest Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8713 and Area Under the Precision-Recall Curve (AUPRC) of 0.5026 on the test set. For Dataset 2, it obtained an AUROC of 0.8551 and an AUPRC of 0.8713 on the test set. The results proved its potential to advance ACP prediction and support drug discovery efforts.