The Role of Artificial Intelligence in the Development of Efflux Pump Inhibitors
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Abstract
Background: Antimicrobial resistance (AMR) mediated by efflux pumps constitutes a critical health problem, necessitating urgent strategies for the development of new efflux pump inhibitors (EPIs). In this regard, artificial intelligence (AI) seems to be an innovative strategy for accelerating discovery, optimization, and understanding of EPIs mechanisms of action.
Conclusion: This review summarizes recent advances regarding the role of AI in the development of new EPI, with emphasis on machine learning (ML) based inhibitor prediction, molecular dynamics (MD) for binding analysis, and quantitative structure-activity relationship modeling (QSAR). By regrouping data from recent studies, we discuss here the important role played by AI in the improvement of lead identification, inhibitor designs, and the study of the resistance mechanisms. Despite current limitations such as limited, fragmented data and structural complexity of efflux pumps, AI offers great promise to revolutionize EPI development. In order to effectively combat AMR, we address here some key approaches, applications, challenges, and future directions, demonstrating the urgent need for interdisciplinary collaboration.
1. Tang KWK, Millar BC, Moore JE. Antimicrobial resistance (AMR). Br J Biomed Sci 2023; 80:11387.
2. Murray CJ, Ikuta KS, Sharara F, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399(10325):629-55.
3. Salam MA, Al-Amin MY, Salam MT, et al. Antimicrobial resistance: a growing serious threat for global public health. Healthcare (Basel) 2023; 11(13):1946.
4. Piddock LJ. Multidrug-resistance efflux pumps - not just for resistance. Nat Rev Microbiol 2006; 4(8):629-36.
5. Lewis BR, Uddin MR, Moniruzzaman M, et al. Conformational restriction shapes the inhibition of a multidrug efflux adaptor protein. Nat Commun 2023; 14(1):39005.
6. Gaurav A, Bakht P, Saini M, et al. Role of bacterial efflux pumps in antibiotic resistance, virulence, and strategies to discover novel efflux pump inhibitors. Microbiology (Reading). 2023; 169(5):001333.
7. Uzochukwu EO, Ezichi FO, Babalola OO, et al. Advances in machine learning for optimizing pharmaceutical drug discovery. Curr Proteomics 2025; 22(2).
8. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020; 180(4):688-702.e13.
9. Bera A, Roy RK, Joshi P, et al. Machine learning-guided discovery of AcrB and MexB efflux pump inhibitors. J Phys Chem B 2024; 128(3):648-63.
10. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18(6):463-77.
11. Zgurskaya HI, Löpez CA, Gnanakaran S. Permeability barrier of Gram-negative cell envelopes and approaches to bypass it. ACS Infect Dis 2015; 1(11):512-22.
12. Sharma A, Gupta VK, Pathania R. Efflux pump inhibitors for bacterial pathogens: from bench to bedside. Indian J Med Res 2019; 149(2):129-45.
13. Novelli M, Bolla JM. RND efflux pump induction: a crucial network unveiling adaptive antibiotic resistance mechanisms of Gram-negative bacteria. Antibiotics (Basel) 2024; 13(6):501.
14. Du D, Wang Z, James NR, et al. Structure of the AcrAB-TolC multidrug efflux pump. Nature 2014; 509(7501):512-5.
15. Du D, Wang-Kan X, Neuberger A, et al. Multidrug efflux pumps: structure, function and regulation. Nat Rev Microbiol 2018; 16(9):523-39.
16. Li XZ, Nikaido H. Efflux-mediated drug resistance in bacteria: an update. Drugs 2010; 69(12):1555-623.
17. World Health Organization. Global antimicrobial resistance and use surveillance system (GLASS) (Internet). Geneva: World Health Organization; 2022 [cited 2025 Sep 30]. Available from: https://iris.who.int/bitstream/handle/10665/364996/9789240062702-eng.pdf?sequence=1
18. Farha AM, Megan MT, Brown ED. Important challenges to finding new leads for new antibiotics. Curr Opin Microbiol 2025; 83:102562.
19. Siasat PA, Blair JMA. Microbial primer: multidrug efflux pumps. Microbiology (Reading). 2023; 169(10):001370.
20. Jabeen F. Application of machine learning and deep learning approaches: cheminformatics for drug discovery [master's thesis]. Ottawa: Carleton University; 2024. Available from: https://carleton.scholaris.ca/server/api/core/bitstreams/27f0e122-5cbe-44dc-a530-7773d8300dea/content
21. Palazzotti D, Felicetti T, Sabatini S, et al. Fighting antimicrobial resistance: insights on how the Staphylococcus aureus NorA efflux pump recognizes 2-phenylquinoline inhibitors by supervised molecular dynamics (SuMD) and molecular docking simulations. J Chem Inf Model 2023; 63(15):4875-87.
22. Irianti DI, Adhika DR, Tanuwidjaja J, et al. Prenylated isoflavonoids from Fabaceae against the NorA efflux pump in Staphylococcus aureus. Sci Rep 2023; 13:22548.
23. Thai KM, Ngo TD, Phan TV, et al. Virtual screening for novel Staphylococcus aureus NorA efflux pump inhibitors from natural products. Med Chem 2015; 11(2):135-55.
24. Liu G, Catacutan DB, Rathod K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol 2023; 19(9):1342-50.
25. Wong F, Zheg EJ, Valeri JA, et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 2024; 626(7997):177-85.
26. Das P, Sercu T, Wadhawan K, et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat Biomed Eng 2021; 5(6):613-23.
27. Phan TV, Nguyen VTV, Nguyen CHH, et al. Discovery of AcrAB-TolC pump inhibitors: virtual screening and molecular dynamics simulation approach. J Biomol Struct Dyn 2023; 41(22):12503-20.
28. Bhat AR, Ahmed S. Artificial intelligence (AI) in drug design and discovery: a comprehensive review. In Silico Res Biomed 2025; 1:100049.
29. Sutanto H, Fetarayani D. Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy. NPJ Drug Discov 2025; 2:25.
30. Venkataraman M, Rao GC, Madavareddi JK, et al. Leveraging machine learning models in evaluating ADMET properties for drug discovery and development. ADMET DMPK 2025; 13(3):2772.
31. Berida TI, Adekunle YA, Dada-Adegbola H, et al. Plant antibacterials: the challenges and opportunities. Heliyon 2024; 10(10):e31145.
32. Wang Y, Venter H, Ma S. Efflux pump inhibitors: a novel approach to combat efflux-mediated drug resistance in bacteria. Curr Drug Targets 2016; 17(6):702-19.
33. Behera DU, Gaur M, Sahoo M, et al. Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance. RSC Med Chem 2024; 15(1):127-38.
34. Omoteso OA, Fadaka AO, Walker RB, et al. Innovative strategies for combating multidrug-resistant tuberculosis: advances in drug delivery systems and treatment. Microorganisms 2025; 13(4):722.
35. Jamshidi S, Sutton JM, Rahman KM. An overview of bacterial efflux pumps and computational approaches to study efflux pump inhibitors. Future Med Chem 2016; 8(2):195-210.
36. Kavanaugh LG, Kollmann CS, Dörr T. Structural and functional diversity of Resistance-Nodulation-Division (RND) efflux pump transporters with implications for antimicrobial resistance. Microbiol Mol Biol Rev 2024; 88(3):e0008923.
37. Li XZ, Plésiat P, Nikaido H. The challenge of efflux-mediated antibiotic resistance in Gram-negative bacteria. Clin Microbiol Rev 2015; 28(2):337-418.
38. Piotto S, Marrafino F, Sessa L, D’Abrosca G, Palumbo R, Basile A, et al. Advancing drug discovery through integrative computational models and AI technologies. In: RExPO24 Conference; 2024.
39. Duffey M, Blasco B, Burrows JN, et al. Extending the potency and lifespan of antibiotics: inhibitors of Gram-negative bacterial efflux pumps. ACS Infect Dis 2024; 10(5):1458-82.
40. Renau TE, Léger R, Filonova L, et al. Conformationally-restricted analogues of efflux pump inhibitors that potentiate the activity of levofloxacin in Pseudomonas aeruginosa. Bioorg Med Chem Lett 2003; 13(16):2755-8.
41. Porto WF, Irazazabal L, Alves ESF, et al. In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design. Nat Commun 2018; 9:1490.
42. Schuster S, Vavra M, Wirth DAN, et al. Comparative reassessment of AcrB efflux inhibitors reveals differential impact of specific pump mutations on the activity of potent compounds. Microbiol Spectr 2024; 12(2):e0304523.
2. Murray CJ, Ikuta KS, Sharara F, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399(10325):629-55.
3. Salam MA, Al-Amin MY, Salam MT, et al. Antimicrobial resistance: a growing serious threat for global public health. Healthcare (Basel) 2023; 11(13):1946.
4. Piddock LJ. Multidrug-resistance efflux pumps - not just for resistance. Nat Rev Microbiol 2006; 4(8):629-36.
5. Lewis BR, Uddin MR, Moniruzzaman M, et al. Conformational restriction shapes the inhibition of a multidrug efflux adaptor protein. Nat Commun 2023; 14(1):39005.
6. Gaurav A, Bakht P, Saini M, et al. Role of bacterial efflux pumps in antibiotic resistance, virulence, and strategies to discover novel efflux pump inhibitors. Microbiology (Reading). 2023; 169(5):001333.
7. Uzochukwu EO, Ezichi FO, Babalola OO, et al. Advances in machine learning for optimizing pharmaceutical drug discovery. Curr Proteomics 2025; 22(2).
8. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020; 180(4):688-702.e13.
9. Bera A, Roy RK, Joshi P, et al. Machine learning-guided discovery of AcrB and MexB efflux pump inhibitors. J Phys Chem B 2024; 128(3):648-63.
10. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18(6):463-77.
11. Zgurskaya HI, Löpez CA, Gnanakaran S. Permeability barrier of Gram-negative cell envelopes and approaches to bypass it. ACS Infect Dis 2015; 1(11):512-22.
12. Sharma A, Gupta VK, Pathania R. Efflux pump inhibitors for bacterial pathogens: from bench to bedside. Indian J Med Res 2019; 149(2):129-45.
13. Novelli M, Bolla JM. RND efflux pump induction: a crucial network unveiling adaptive antibiotic resistance mechanisms of Gram-negative bacteria. Antibiotics (Basel) 2024; 13(6):501.
14. Du D, Wang Z, James NR, et al. Structure of the AcrAB-TolC multidrug efflux pump. Nature 2014; 509(7501):512-5.
15. Du D, Wang-Kan X, Neuberger A, et al. Multidrug efflux pumps: structure, function and regulation. Nat Rev Microbiol 2018; 16(9):523-39.
16. Li XZ, Nikaido H. Efflux-mediated drug resistance in bacteria: an update. Drugs 2010; 69(12):1555-623.
17. World Health Organization. Global antimicrobial resistance and use surveillance system (GLASS) (Internet). Geneva: World Health Organization; 2022 [cited 2025 Sep 30]. Available from: https://iris.who.int/bitstream/handle/10665/364996/9789240062702-eng.pdf?sequence=1
18. Farha AM, Megan MT, Brown ED. Important challenges to finding new leads for new antibiotics. Curr Opin Microbiol 2025; 83:102562.
19. Siasat PA, Blair JMA. Microbial primer: multidrug efflux pumps. Microbiology (Reading). 2023; 169(10):001370.
20. Jabeen F. Application of machine learning and deep learning approaches: cheminformatics for drug discovery [master's thesis]. Ottawa: Carleton University; 2024. Available from: https://carleton.scholaris.ca/server/api/core/bitstreams/27f0e122-5cbe-44dc-a530-7773d8300dea/content
21. Palazzotti D, Felicetti T, Sabatini S, et al. Fighting antimicrobial resistance: insights on how the Staphylococcus aureus NorA efflux pump recognizes 2-phenylquinoline inhibitors by supervised molecular dynamics (SuMD) and molecular docking simulations. J Chem Inf Model 2023; 63(15):4875-87.
22. Irianti DI, Adhika DR, Tanuwidjaja J, et al. Prenylated isoflavonoids from Fabaceae against the NorA efflux pump in Staphylococcus aureus. Sci Rep 2023; 13:22548.
23. Thai KM, Ngo TD, Phan TV, et al. Virtual screening for novel Staphylococcus aureus NorA efflux pump inhibitors from natural products. Med Chem 2015; 11(2):135-55.
24. Liu G, Catacutan DB, Rathod K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol 2023; 19(9):1342-50.
25. Wong F, Zheg EJ, Valeri JA, et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 2024; 626(7997):177-85.
26. Das P, Sercu T, Wadhawan K, et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat Biomed Eng 2021; 5(6):613-23.
27. Phan TV, Nguyen VTV, Nguyen CHH, et al. Discovery of AcrAB-TolC pump inhibitors: virtual screening and molecular dynamics simulation approach. J Biomol Struct Dyn 2023; 41(22):12503-20.
28. Bhat AR, Ahmed S. Artificial intelligence (AI) in drug design and discovery: a comprehensive review. In Silico Res Biomed 2025; 1:100049.
29. Sutanto H, Fetarayani D. Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy. NPJ Drug Discov 2025; 2:25.
30. Venkataraman M, Rao GC, Madavareddi JK, et al. Leveraging machine learning models in evaluating ADMET properties for drug discovery and development. ADMET DMPK 2025; 13(3):2772.
31. Berida TI, Adekunle YA, Dada-Adegbola H, et al. Plant antibacterials: the challenges and opportunities. Heliyon 2024; 10(10):e31145.
32. Wang Y, Venter H, Ma S. Efflux pump inhibitors: a novel approach to combat efflux-mediated drug resistance in bacteria. Curr Drug Targets 2016; 17(6):702-19.
33. Behera DU, Gaur M, Sahoo M, et al. Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance. RSC Med Chem 2024; 15(1):127-38.
34. Omoteso OA, Fadaka AO, Walker RB, et al. Innovative strategies for combating multidrug-resistant tuberculosis: advances in drug delivery systems and treatment. Microorganisms 2025; 13(4):722.
35. Jamshidi S, Sutton JM, Rahman KM. An overview of bacterial efflux pumps and computational approaches to study efflux pump inhibitors. Future Med Chem 2016; 8(2):195-210.
36. Kavanaugh LG, Kollmann CS, Dörr T. Structural and functional diversity of Resistance-Nodulation-Division (RND) efflux pump transporters with implications for antimicrobial resistance. Microbiol Mol Biol Rev 2024; 88(3):e0008923.
37. Li XZ, Plésiat P, Nikaido H. The challenge of efflux-mediated antibiotic resistance in Gram-negative bacteria. Clin Microbiol Rev 2015; 28(2):337-418.
38. Piotto S, Marrafino F, Sessa L, D’Abrosca G, Palumbo R, Basile A, et al. Advancing drug discovery through integrative computational models and AI technologies. In: RExPO24 Conference; 2024.
39. Duffey M, Blasco B, Burrows JN, et al. Extending the potency and lifespan of antibiotics: inhibitors of Gram-negative bacterial efflux pumps. ACS Infect Dis 2024; 10(5):1458-82.
40. Renau TE, Léger R, Filonova L, et al. Conformationally-restricted analogues of efflux pump inhibitors that potentiate the activity of levofloxacin in Pseudomonas aeruginosa. Bioorg Med Chem Lett 2003; 13(16):2755-8.
41. Porto WF, Irazazabal L, Alves ESF, et al. In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design. Nat Commun 2018; 9:1490.
42. Schuster S, Vavra M, Wirth DAN, et al. Comparative reassessment of AcrB efflux inhibitors reveals differential impact of specific pump mutations on the activity of potent compounds. Microbiol Spectr 2024; 12(2):e0304523.
| Files | ||
| Issue | Vol 14 No 1 (2026) | |
| Section | Review Articles | |
| Keywords | ||
| Artificial intelligence Antimicrobial resistance Drug discovery Efflux pumps Inhibitors. | ||
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How to Cite
1.
Najoua E, Chaimaa K, Hanane AH. The Role of Artificial Intelligence in the Development of Efflux Pump Inhibitors. J Med Bacteriol. 2026;14(1):62-72.
