ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction

IntroductionCancer is a leading cause of mortality worldwide. Anticancer peptides (ACPs) are promising therapeutic candidates due to their low toxicity, favorable biocompatibility, and selective anticancer activity; however, experimental ACP identification and screening remain labor-intensive, time-consuming, and costly.MethodsWe developed ProtT5-MSCRNet, an end-to-end deep learning framework for ACP prediction that integrates ProtT5-based evolutionary representations, multi-scale convolutional