ACP‐DPE: A Dual‐Channel Deep Learning Model for Anticancer Peptide Prediction

• DOI: 10.1049/syb2.70010
• Publication Date: 2025-03-23T15:40:46Z
• Authors: Guohua Huang; Yujie Cao; Qi Dai; Weihong Chen

ABSTRACTCancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectively treating cancer. Identifying ACPs is challenging due to the limitation of experimental conditions. To address this, we proposed a dual‐channel‐based deep learning method, termed ACP‐DPE, for ACP prediction. The ACP‐DPE consisted of two parallel channels: one was an embedding layer followed by the bi‐directional gated recurrent unit (Bi‐GRU) module, and the other was an adaptive embedding layer followed by the dilated convolution module. The Bi‐GRU module captured the peptide sequence dependencies, whereas the dilated convolution module characterised the local relationship of amino acids. Experimental results show that ACP‐DPE achieves an accuracy of 82.81% and a sensitivity of 86.63%, surpassing the state‐of‐the‐art method by 3.86% and 5.1%, respectively. These findings demonstrate the effectiveness of ACP‐DPE for ACP prediction and highlight its potential as a valuable tool in cancer treatment research.