MA‐PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

Abstract

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time‐consuming and costly nature of wet‐lab discriminatory methods has spurred the development of various machine learning and deep learning‐based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA‐PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular‐level chemical features and sequence information of peptides, MA‐PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA‐PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA‐PEP are available at https://github.com/liangxiaodata/MA-PEP.