Identifying Anticancer Peptide via a Semi-Supervised Prediction Framework with Bio-Inspired Augmentation

Abstract

Anticancer peptides (ACPs) have emerged as a promising class of therapeutic molecules due to their low toxicity and high specificity. However, experimentally identifying ACPs is labor-intensive and low-throughput, making virtual screening with intelligent models an attractive alternative. Despite recent progress, the number of ACP samples available in existing databases remains limited, which may cause predictive models to overfit the labeled information and exhibit suboptimal generalization. To address this issue, we propose a semi-supervised prediction framework with bio-inspired data augmentation for ACP identification, called AugACP. Specifically, we incorporate semantic knowledge into masked Transformer pretraining to guide ACP sequence augmentation and provide more reliable supervised signals. Based on the augmented data, we adopt a semi-supervised learning strategy that selects high-confidence pseudo-labels from augmented peptides to improve prediction performance. In the computational experiments, AugACP outperforms state-of-the-art baseline methods under various experimental configurations, particularly in generalizing to unseen oligopeptides. Moreover, t-SNE visualizations indicate that AugACP learns more discriminative embeddings for separating ACPs from non-ACPs, while external validation on novel ACPs also achieves an F1-score of 0.9272. These results highlight the predictive power of AugACP and its potential value in promoting anticancer drug discovery.