EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

Journal: Current medicinal chemistry
PMID: 38494930

Abstract

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-CoV) and SARS-CoV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study.

Authors

  • Shengli Zhang
    Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University Beijing, China.
  • Yuanyuan Jing
    School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
  • Yunyun Liang
    School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China. Electronic address: yunyunliang88@163.com.

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