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 fo

SUBMITTER: Kang L 

PROVIDER: S-EPMC12629234 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature