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Volume 32, Issue 10
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP.

Methods

In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of -spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively.

Results

The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models.

Conclusion

The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

© 2025 Bentham Science Publishers
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2025-03-31

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/content/journals/cmc/10.2174/0109298673277663240101111507
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iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network
Curr. Med. Chem. 32, 2055 (2025); https://doi.org/10.2174/0109298673277663240101111507
/content/journals/cmc/10.2174/0109298673277663240101111507
/content/journals/cmc/10.2174/0109298673277663240101111507
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  • Article Type:     Research Article
Keyword(s): Anti-coronavirus peptide; attention mechanism; bidirectional gated recurrent unit; long-short term memory; multiple features; recurrent neural network

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