MFE-ACVP: anti-coronavirus peptide prediction based on multimodal feature extraction and ensemble learning.

Abstract

The COVID-19 pandemic poses a serious threat to global public health. Anti-coronavirus peptides (ACVPs) exhibit high targeting, low toxicity, and excellent modifiability, making them promising candidates for antiviral drug discovery. These properties offer advantages over traditional small-molecule drugs. To reduce the time and cost spent on large-scale peptide screening and activity validation, there is an urgent need to construct efficient artificial intelligence models that assist in the identification of ACVPs. However, the limited number of experimentally validated ACVPs severely constrains the generalization ability and prediction accuracy of existing computational models. In this study, a new prediction framework, the Multi-modal Feature Extraction and Ensemble learning framework for Anti-Coronavirus Peptide prediction(MFE-ACVP), is proposed for identifying potential candidate peptides for ACVPs. The method generates high-quality ACVPs by introducing an improved Generative Adversarial Network (GAN) with materialization constraints to alleviate the problem of data insufficiency. Simultaneously, fusing sequence, structural, evolutionary, and topological features, we constructed a 100-dimensional cross-scale feature representation. An ensemble architecture integrating five traditional machine learning models with deep neural networks (DNNs) was designed to enhance predictive performance. Compared with the existing models PreAntiCoV, iACVP, ACVPred, and ENNAVIA-C/D, MFE-ACVP achieved 86.37%, 77.62%, and 65.19% of area under the curve (AUC), accuracy (ACC), and Matthew's correlation coefficient (MCC), respectively, on an independent validation set, showing superior predictive performance and stability. To facilitate ACVP screening, we developed a publicly accessible web server http://bioprediction.sa1.tunnelfrp.com/.