Multi-ACPNet: A multi-scale sequence-structure feature fusion framework for anticancer peptide identification and functional prediction.

Anticancer peptides (ACPs) have emerged as promising therapeutic candidates for cancer treatment due to their high efficacy and low propensity for inducing drug resistance. However, existing ACP identification methods primarily rely on peptide sequence features while neglecting spatial structural characteristics. Moreover, few approaches can simultaneously predict the functional activity of ACPs. To address these limitations, this study proposes Multi-ACPNet, a novel dual-function predictor capable of both ACP identification and activity type classification. This model innovatively integrates sequence and structural features through a multi-stage framework. It employs a hybrid Bidirectional Long Short-Term Memory (BiLSTM) and causal convolutional network to capture both long-range dependencies and local sequence patterns, followed by a multi-scale Graph Convolutional Network (GCN) that dynamically fuses local and long-range structural dependencies using residual connections and adaptive weighting. Experimental results demonstrate that Multi-ACPNet achieves outstanding performance, with Accuracy of 0.8140, 0.9536, and 0.8770 on three benchmark datasets for ACP identification. For functional prediction, it attains an AUC of 0.9033, F1-score of 0.8472, and Hamming loss of 0.1303, significantly outperforming state-of-the-art predictors.