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

Abstract

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.