Design of Highly Potent Antibiofilm, Antimicrobial Peptides Using Explainable Artificial Intelligence

Antimicrobial peptides have emerged as a potential alternative to traditional small-molecule antimicrobials. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of biofilms. However, advanced rational peptide design methods are still required to ensure optimal property profiles of such peptides, while limiting the cost of their synthesis and screening. Here, we present a computational pipeline for the rational de novo design of antimicrobial and antibiofilm peptides based on an explainable artificial intelligence (XAI) framework. The developed framework combines a Wasserstein Autoencoder (WAE) and a nonlinear dimensionality reduction method─generative topographic mapping (GTM). The WAE was used to learn the latent representation of the peptide space, while the GTM guided the generation of novel AMPs through an illustrative depiction of the latent space in the form of 2D maps. The generated peptides were subjected to screening by machine learning models, resulting in the final hit list based on their predicted activity. The efficacy of the peptides generated with the developed pipeline was experimentally verified by synthesis and testing for activity against methicillin-resistant <i>Staphylococcus aureus</i> (MRSA), achieving a 100% hit rate in targeting biofilms. Notably, the most potent antibiofilm peptide developed in this study demonstrated almost one order of magnitude improvement in IC₅₀ value compared with the potent antibiofilm peptide reference "1018", used as a positive control. The developed pipeline is readily extendable for the optimization of additional peptide properties, including cytotoxicity, tendency to aggregate, and proteolytic stability, underscoring its potential utility for rational design of the peptide-based therapeutics.