The research presents a groundbreaking approach to identifying AMPs by introducing the innovative StackAMP ensemble classifier. Overcoming the limitations of traditional, resource-intensive methods, the study employs five distinct feature extraction methods and four machine learning algorithms to comprehensively analyze peptide sequences. The exceptional performance of StackAMP, achieving 99.97% accuracy, 99.93% specificity, and 100% sensitivity, marks a significant advancement in the field. This research not only contributes to the understanding of AMP recognition but also introduces a practical tool with broad applications in drug discovery, biotechnology, and disease prevention. The potential of StackAMP to revolutionize the rapid and accurate identification of AMPs in diverse biological contexts highlights its transformative impact on healthcare, biotechnological advancements, and the development of novel therapeutic strategies.

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of pept...