ACP-CLB: An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models

Cancer affects millions globally, and as research advances, our understanding and treatment of cancer evolve. Compared to conventional treatments with significant side effects, anticancer peptides (ACPs) have gained considerable attention. Validating ACPs through wet-lab experiments is time-consuming and costly. However, numerous artificial intelligence methods are now used for ACP identification and classification. These methods typically apply a uniform strategy to all feature types, overlooking the potential benefits of more specialized processing for different feature types. In this paper, we propose a framework based on multichannel discriminative processing, where different neural networks are applied to process various feature types, optimizing their respective feature vectors. Additionally, we leverage Large Pretrained Protein Language Models to capture deeper sequence features, further enhancing the model's performance. Contributions: To better validate the overall performance and generalization ability of the model, we compared it with state-of-the-art models using four different data sets (AntiCp2Main, AntiCp2 Alternate, ACP740, cACP-DeepGram). The results show significant improvements across most metrics. Additionally, our proposed framework better assists researchers in distinguishing and identifying ACPs and further validates the need for distinct processing methods for different feature types.