ArticleComputational and structural biotechnology journal2024
ACVPICPred: Inhibitory activity prediction of anti-coronavirus peptides based on artificial neural network.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.Interdisciplinary sciences, computational life sciences · 2026Article
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9 authors.
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Abstract
Peptides, as small molecular compounds, exhibit prominent advantages in the inhibition of coronaviruses due to their safety, efficacy, and specificity, holding great promise as drugs against coronaviruses. The rapid and efficient determination of the activity of anti-coronavirus peptides (ACovPs) can greatly accelerate the development of drugs for treating coronavirus-related diseases. Hence, we present ACVPICPred, a computational model designed to predict the inhibitory activity of ACovPs based on their sequences and structural information. By leveraging bioinformatics tools AlphaFold3 for structural predictions and several feature extraction methods, the model integrates both sequence and structural features to enhance prediction accuracy. To address the limitations of existing datasets, we employed data augmentation techniques, including the introduction of noise and the SMOGN, to improve the model robustness. The model's performance was evaluated through five-fold cross-validation, achieving a Pearson correlation coefficient of 0.7668 (
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