HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis

Cancer remains a major contributor to global mortality, constituting a significant and escalating threat to human health. Anticancer peptides (ACPs) have emerged as promising therapeutic agents due to their specific mechanisms of action, pronounced tumor-targeting capability, and low toxicity. Nevertheless, traditional approaches for ACP identification are constrained by their reliance on shallow, hand-crafted sequence features, which fail to capture deeper semantic and structural characteristics. Moreover, such models exhibit limited robustness and interpretability when confronted with practical challenges such as severe class imbalance. To address these limitations, this study proposes HyperACP, an innovative framework for ACP recognition that integrates deep representation learning, adaptive sampling, and mechanistic interpretability. The framework leverages the ESMC protein language model to extract comprehensive sequence features and employs a novel adaptive algorithm, ANBS, to mitigate class imbalance at the decision boundary. For enhanced model transparency, SHAP-Res is incorporated to elucidate the contributions of individual residues to the final predictions. Comprehensive evaluations demonstrate that HyperACP consistently outperforms state-of-the-art methods across multiple datasets and validation protocols—including 10-fold cross-validation and independent test sets—according to metrics such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). Furthermore, the model yields biologically interpretable results, pinpointing key residues (K, L, F, G) known to play pivotal roles in anticancer activity. These findings provide not only a robust predictive tool (available at www.hyperacp.com) but also novel insights into the structure-function relationships underlying ACPs.Author summary: Accurately identifying anticancer peptides (ACPs) is crucial for the discovery of next-generation cancer therapeutics, but existing computational methods often struggle with feature limitations, data imbalance, and poor interpretability. In this study, we present HyperACP—a novel computational framework that combines advanced deep protein representation, an adaptive sampling algorithm, and an ensemble learning strategy to systematically address these challenges. HyperACP utilizes a cutting-edge protein language model to capture the complex structural and functional information embedded in peptide sequences. Our newly designed sampling method generates informative minority class samples in challenging boundary regions, improving the model’s robustness to imbalanced data. Most importantly, we introduce SHAP-Res, a residue-level interpretability module that reveals how individual amino acids drive model predictions and links computational insights directly to biological function. HyperACP not only achieves state-of-the-art prediction accuracy but also provides transparent and biologically meaningful explanations, paving the way for more reliable and interpretable peptide-based drug discovery.