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<p><i>Methicillin-resistant Staphylococcus aureus</i> (MRSA) is a severe worldwide health issue owing to its resistance against the vast majority of β-lactam antibiotics as well as its ability to bypass traditional therapies. The review herein emphasizes the promise of indole-based compounds as new antimicrobial agents against MRSA, highlighting their mechanism of action, especially the inhibition of penicillin-binding protein 2a (PBP2a) produced by the mecA gene. Structure–activity relationship (SAR) studies show that changes like the substitution of bromine at the C-5 position of indole, introduction of electron-withdrawing groups, and the optimal lengths of linkers increase antibacterial activity considerably. Advanced hybrid indoles such as oxindole-nitroimidazole and tris-indole skeletons were also highly active against MRSA with MIC values as low as 0.0625&#xa0;µg/mL. Notably, the article also addresses the role of emerging Artificial Intelligence (AI) and Machine Learning (ML) in expediting the discovery and optimization of antimicrobial compounds, such as resistance pattern prediction and de novo design of efficacious indole-based therapeutics. Collectively, these findings indicate a significant synergy between medicinal chemistry and computational tools in combating antibiotic resistance.</p> Graphical abstract <p></p>

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Indole derivatives as antibacterials: overcoming MRSA resistance through SAR insights and AI-driven design

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Satyam Yadav,
  • Priyanshu Nema,
  • Shivam Kori,
  • Sushil Kumar Kashaw

摘要

Methicillin-resistant Staphylococcus aureus (MRSA) is a severe worldwide health issue owing to its resistance against the vast majority of β-lactam antibiotics as well as its ability to bypass traditional therapies. The review herein emphasizes the promise of indole-based compounds as new antimicrobial agents against MRSA, highlighting their mechanism of action, especially the inhibition of penicillin-binding protein 2a (PBP2a) produced by the mecA gene. Structure–activity relationship (SAR) studies show that changes like the substitution of bromine at the C-5 position of indole, introduction of electron-withdrawing groups, and the optimal lengths of linkers increase antibacterial activity considerably. Advanced hybrid indoles such as oxindole-nitroimidazole and tris-indole skeletons were also highly active against MRSA with MIC values as low as 0.0625 µg/mL. Notably, the article also addresses the role of emerging Artificial Intelligence (AI) and Machine Learning (ML) in expediting the discovery and optimization of antimicrobial compounds, such as resistance pattern prediction and de novo design of efficacious indole-based therapeutics. Collectively, these findings indicate a significant synergy between medicinal chemistry and computational tools in combating antibiotic resistance.

Graphical abstract