Machine learning-driven discovery of antimicrobial peptides against

Introduction: Antibiotic resistance has become a global health crisis, driving the urgent need for novel antibacterial strategies. Antimicrobial peptides (AMPs) have emerged as promising therapeutic candidates due to their broad-spectrum activity and low propensity for resistance development. However, conventional discovery approaches remain time-consuming and inefficient for large-scale screening. Methods: In this study, we developed an integrated prediction framework that combines deep learning with classical machine learning methods to enable rapid and accurate identification of AMPs. The model was applied to screen over 250,000 artificially generated peptide sequences. Candidate peptides were selected based on physicochemical descriptors and activity-associated features, followed by solid-phase synthesis and minimum inhibitory concentration assays against Pseudomonas aeruginosa. Mechanistic investigations were conducted using scanning electron microscopy to visualize membrane damage, molecular dynamics simulations to probe peptide-membrane interactions, and transcriptomic profiling to assess bacterial stress responses and metabolic pathway alterations. Results: Ten promising antibacterial candidates were successfully identified and experimentally validated. All selected peptides exhibited measurable activity against P. aeruginosa, with several showing potent inhibitory effects. Microscopic and simulation analyses confirmed that the peptides exert their antibacterial effects primarily through membrane disruption. Transcriptomic data further revealed that these peptides interfere with key metabolic pathways and activate bacterial stress response systems. Discussion: Our findings demonstrate that the integrated deep learning-machine learning pipeline offers an efficient and reliable approach for large-scale AMP discovery. The mechanistic insights gained from this study not only validate the predicted candidates but also provide a foundation for rational optimization of peptide-based therapeutics. This comprehensive strategy holds promise for accelerating the development of next-generation alternatives to conventional antibiotics.