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ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide
Zhi Li1,2, Zhen Liu2,3, Jianguo Zhong2
1The First People's Hospital of Jiashan, Jiashan Hospital Affiliated of Jiaxing University, Jiashan, China.
Introduction:
Cancer 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.
Methods:
We developed ProtT5-MSCRNet, an end-to-end deep learning framework for ACP prediction that integrates ProtT5-based evolutionary representations, multi-scale convolutional feature extraction, channel-wise attention recalibration, and robust optimization strategies.
Results:
Experiments on two independent benchmark datasets showed that ProtT5-MSCRNet outperformed state-of-the-art ACP prediction methods. The model achieved ACC/SN/SP/MCC values of 0.954/0.874/0.983/0.881 on Test Set 1 and 0.984/0.980/0.987/0.967 on Test Set 2. Ablation studies and visualization analyses further supported the effectiveness and interpretability of the proposed model.
Discussion:
ProtT5-MSCRNet provides a robust, accurate, and biologically meaningful computational tool for facilitating ACP identification and accelerating anticancer peptide discovery.
