Novel Fuzzy Approach to Antimicrobial Peptide Activity Prediction: A Tale of Limited and Imbalanced Data That Models Won’t Hear

Abstract

Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-generation anti-cancer treatment regimes, etc. Owing to the significant cost and time required for wet lab-based AMP screening, researchers have framed the task as an ML problem. However, traditional models rely on the unrealistic premise of large medical data availability to achieve significant performance levels; otherwise, they overfit, decreasing model precision. The collection of such labeled medical data is a challenging and expensive task in itself. The current study is the first to examine models in a real-world setting, training them on restricted and highly imbalanced data. A Fuzzy Intelligence based model is proposed for short (<30 aa) AMP activity prediction, and its ability to learn on limited and severely skewed high-dimensional space mapping is demonstrated over a set of experiments. The proposed model significantly outperforms state-of-the-art ML models trained on the same data. The findings demonstrate the model's efficacy as a potential method for in silico AMP activity prediction.