Comparative Analysis of Machine Learning Approaches for Antimicrobial Peptide Prediction

Abstract

Antimicrobial resistance (AMR) is a global issue due to improper drug use in humans and animals. Antimicrobial peptides (AMPs) show promise in targeting bacteria with minimal harm to host cells and low risk of resistance development. Machine learning enhances accuracy in predicting AMPs. Common classifiers include SVM, RF, ANN, LGBM, and DT. This review compares peptide prediction tools based on machine learning, assessing performance using cross-validation. Carefully chosen independent datasets were used to evaluate predictive efficiency. By utilizing a variety of ML methods, the best techniques for predicting Antimicrobial peptides, Antibacterial peptides, Antifungal peptides can be developed quickly