MFE-ACVP: anti-coronavirus peptide prediction based on multimodal feature
extraction and ensemble learning
#MMPMID41259417
Kang L
; Bao L
; Zhang P
; Yu X
; Zhang L
; Zhou W
; Bai Y
Brief Bioinform
2025[Nov]; 26
(6
): ? PMID41259417
show ga
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/.
|*Antiviral Agents/pharmacology/chemistry/therapeutic use
[MESH]
free
free
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