Background: Multidrug-resistant (MDR) methicillin-resistant Staphylococcus aureus (MRSA) has become a prime health concern globally. These bacteria are found in hospital areas where they are regularly dealing with antibiotics. This brings many possibilities for its mutation, so drug resistance occurs.
Introduction: Nowadays, these nosocomial MRSA strains spread into the community and live stocks. Resistance in Staphylococcus aureus is due to mutations in their genetic elements.
Methods: As the bacteria become resistant to antibiotics, new approaches like antimicrobial peptides (AMPs) play a vital role and are more efficacious, economical, time, and energy saviours.
Results: Machine learning approaches of Artificial Intelligence are the in silico technique which has their importance in better prediction, analysis, and fetching of important details regarding AMPs.
Conclusion: Anti-microbial peptides could be the next-generation solution to combat drug resistance among Superbugs. For better prediction and analysis, implementing the in silico technique is beneficial for fast and more accurate results.
Keywords: MRSA; Multiple drug resistance; antimicrobial peptide; artificial intelligence; computational approach; peptide design.
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