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Important Progress in Antimicrobial Peptide Prediction Research in the Past Five Years
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DOI：10.1016/j.ab.2026.116141.png)
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Abstract
En 中文
• Help researchers better understand antimicrobial peptide prediction and its underlying mechanisms through comprehensive analysis of computational approaches • Using computational methods to predict antimicrobial peptides has lower costs and higher efficiency compared to experimental validation • Systematically reviews databases and summarizes methodological innovations from 2020-2024 • Compares differences of models in algorithm design and feature extraction across structure-based methods, traditional machine learning, deep learning, and deep generative models • Discusses current limitations in model interpretability, multi-dimensional feature integration, and class imbalance, proposing future research directions
Keywords:
Antimicrobial peptide prediction
Computational methods
Machine learning
Deep learning
Feature extraction
Journal
IF:
2.5
Papers:
272
Citations:
3.3W
Organization
Cited Papers