Authors : Anil Kumar; Aman Sharma; Arzoo Imam; Abhilasha Devi

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/36n32rxd

Scribd : https://tinyurl.com/53dz8trk

DOI : https://doi.org/10.38124/ijisrt/25oct418

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Abstract : The increasing occurrence of multidrug-resistant (MDR) bacteria, commonly known as superbugs. It is a leading global health threat. The antibiotic discovery pipeline is effectively stagnant due to excessive costs, a long lead time for drug development, and decreased profits for pharmaceutical companies. Artificial intelligence (AI) and machine learning (ML) have proven to be thriving zeitgeists for advancing antimicrobial research through the rapid evaluations of large biological and chemical datasets, predicting antimicrobial activity, identifying novel drug targets, and optimizing pharmacokinetics. This review outlines the various applications of AI-based endeavours in solving the issue of MDR pathogens. These include target identification, virtual screenings, de novo drug design, drug repurposing, optimizing pharmacokinetics, and integrating with experimental systems biology. We will discuss significant discoveries such as halicin and abaucin, as well as limitations including data availability and interpretability. We will explore regulatory aspects and ethical aspects of AI and ML applications, and we will propose future directions for integrating AI and ML in clinical microbiology and personalized medicine to subsume the global antimicrobial resistance (AMR) crisis.

References :

1. Ahn, N. G., & Wang, A. H.-J. (2008). Proteomics and genomics: Perspectives on drug and target  discovery. Current Opinion in Chemical Biology, 12(1), 1–3. https://doi.org/10.1016/j.cbpa.2008.02.016
2. Arnold, A., McLellan, S., & Stokes, J. M. (2025). How AI can help us beat AMR. Npj Antimicrobials and Resistance, 3, 18. https://doi.org/10.1038/s44259-025-00085-4
3. Awan, R. E., Zainab, S., Yousuf, F. J., & Mughal, S. (2024). AI-driven drug discovery: Exploring Abaucin as a promising treatment against multidrug-resistant Acinetobacter baumannii. Health Science Reports, 7(6), e2150. https://doi.org/10.1002/hsr2.2150
4. Bi, X., Wang, Y., Wang, J., & Liu, C. (2025). Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology. Pharmaceutics, 17(9), 1186. https://doi.org/10.3390/pharmaceutics17091186
5. Brown, E. D., & Wright, G. D. (2016). Antibacterial drug discovery in the resistance era. Nature, 529(7586), 336–343. https://doi.org/10.1038/nature17042
6. Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente-Nunez, C. (2025). Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance. Npj Antimicrobials and Resistance, 3, 2. https://doi.org/10.1038/s44259-024-00068-x
7. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018a). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
8. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018b). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
9. Chen, L., Li, S., & She, Z. (2025). A study on the impact of artificial intelligence applications on corporate green technological innovation: A mechanism analysis from multiple perspectives. International Review of Economics & Finance, 103, 104490. https://doi.org/10.1016/j.iref.2025.104490
10. Cosconati, S., Forli, S., Perryman, A. L., Harris, R., Goodsell, D. S., & Olson, A. J. (2010). Virtual Screening with AutoDock: Theory and Practice. Expert Opinion on Drug Discovery, 5(6), 597–607. https://doi.org/10.1517/17460441.2010.484460
11. Derraz, B., Breda, G., Kaempf, C., Baenke, F., Cotte, F., Reiche, K., Köhl, U., Kather, J. N., Eskenazy, D., & Gilbert, S. (2024). New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. NPJ Precision Oncology, 8, 23. https://doi.org/10.1038/s41698-024-00517-w
12. DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012
13. Dubey, A. K. (n.d.). Soil Fungi: Diversity and Pathogenicity.
14. Eddershaw, P. J., Beresford, A. P., & Bayliss, M. K. (2000). ADME/PK as part of a rational approach to drug discovery. Drug Discovery Today, 5(9), 409–414. https://doi.org/10.1016/S1359-6446(00)01540-3
15. Elalouf, A., Elalouf, H., Rosenfeld, A., & Maoz, H. (2025). Artificial intelligence in drug resistance management. 3 Biotech, 15(5), 126. https://doi.org/10.1007/s13205-025-04282-w
16. Gangwal, A., Ansari, A., Ahmad, I., Azad, A. K., Kumarasamy, V., Subramaniyan, V., & Wong, L. S. (2024). Generative artificial intelligence in drug discovery: Basic framework, recent advances, challenges, and opportunities. Frontiers in Pharmacology, 15, 1331062. https://doi.org/10.3389/fphar.2024.1331062
17. Gangwal, A., & Lavecchia, A. (2024a). Unleashing the power of generative AI in drug discovery. Drug Discovery Today, 29(6), 103992. https://doi.org/10.1016/j.drudis.2024.103992
18. Gangwal, A., & Lavecchia, A. (2024b). Unleashing the power of generative AI in drug discovery. Drug Discovery Today, 29(6), 103992. https://doi.org/10.1016/j.drudis.2024.103992
19. Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., Aliper, A., Ren, F., & Zhavoronkov, A. (2023). Chemistry42: An AI-Driven Platform for Molecular Design and Optimization. Journal of Chemical Information and Modeling, 63(3), 695–701. https://doi.org/10.1021/acs.jcim.2c01191
20. James, K., & Muñoz-Muñoz, J. (n.d.). Computational Network Inference for Bacterial Interactomics. mSystems, 7(2), e01456-21. https://doi.org/10.1128/msystems.01456-21
21. Jarallah, S. J., Almughem, F. A., Alhumaid, N. K., Fayez, N. A., Alradwan, I., Alsulami, K. A., Tawfik, E. A., & Alshehri, A. A. (2025). Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation. International Journal of Pharmaceutics, 680, 125789. https://doi.org/10.1016/j.ijpharm.2025.125789
22. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
23. Kandoi, G., Acencio, M. L., & Lemke, N. (2015). Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review. Frontiers in Physiology, 6, 366. https://doi.org/10.3389/fphys.2015.00366
24. Kim, J., Cheong, Y. E., Jung, I., & Kim, K. H. (2019). Metabolomic and Transcriptomic Analyses of Escherichia coli for Efficient Fermentation of L-Fucose. Marine Drugs, 17(2), 82. https://doi.org/10.3390/md17020082
25. Kulkarni, V. S., Alagarsamy, V., Solomon, V. R., Jose, P. A., & Murugesan, S. (2023). Drug Repurposing: An Effective Tool in Modern Drug Discovery. Russian Journal of Bioorganic Chemistry, 49(2), 157–166. https://doi.org/10.1134/S1068162023020139
26. Kushwaha, A., Kumar, A., & Sharma, A. (2025). Bacterial Spectrum and Antibiotic Resistance Pattern in Urinary Tract Infection Cases at a Tertiary Care Hospital. International Journal of Science and Research (IJSR), 755–760. https://doi.org/10.21275/SR25916213348
27. Li, Y., Meng, Q., Yang, M., Liu, D., Hou, X., Tang, L., Wang, X., Lyu, Y., Chen, X., Liu, K., Yu, A.-M., Zuo, Z., & Bi, H. (2019). Current trends in drug metabolism and pharmacokinetics. Acta Pharmaceutica Sinica. B, 9(6), 1113–1144. https://doi.org/10.1016/j.apsb.2019.10.001
28. Liu, B., He, H., Luo, H., Zhang, T., & Jiang, J. (2019). Artificial intelligence and big data facilitated targeted drug discovery. Stroke and Vascular Neurology, 4(4), 206–213. https://doi.org/10.1136/svn-2019-000290
29. Machtel, P., Bąkowska-Żywicka, K., & Żywicki, M. (2016). Emerging applications of riboswitches – from antibacterial targets to molecular tools. Journal of Applied Genetics, 57(4), 531–541. https://doi.org/10.1007/s13353-016-0341-x
30. McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J., Bhullar, K., Canova, M. J., De Pascale, G., Ejim, L., Kalan, L., King, A. M., Koteva, K., Morar, M., Mulvey, M. R., O’Brien, J. S., Pawlowski, A. C., Piddock, L. J. V., Spanogiannopoulos, P., … Wright, G. D. (2013). The Comprehensive Antibiotic Resistance Database. Antimicrobial Agents and Chemotherapy, 57(7), 3348–3357. https://doi.org/10.1128/AAC.00419-13
31. McCoubrey, L. E., Elbadawi, M., Orlu, M., Gaisford, S., & Basit, A. W. (n.d.). Harnessing machine learning for development of microbiome therapeutics. Gut Microbes, 13(1), 1872323. https://doi.org/10.1080/19490976.2021.1872323
32. Mostafa, F., & Chen, M. (2024). Computational models for predicting liver toxicity in the deep learning era. Frontiers in Toxicology, 5, 1340860. https://doi.org/10.3389/ftox.2023.1340860
33. Navarro-López, D. E., Perfecto-Avalos, Y., Zavala, A., de Luna, M. A., Sanchez-Martinez, A., Ceballos-Sanchez, O., Tiwari, N., López-Mena, E. R., & Sanchez-Ante, G. (2024). Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles. Antibiotics, 13(3), 220. https://doi.org/10.3390/antibiotics13030220
34. Opal, S. M. (2016). Non-antibiotic treatments for bacterial diseases in an era of progressive antibiotic resistance. Critical Care, 20, 397. https://doi.org/10.1186/s13054-016-1549-1
35. Pathan, I., Raza, A., Sahu, A., Joshi, M., Sahu, Y., Patil, Y., Raza, M. A., & Ajazuddin. (2025). Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction. Medicine in Drug Discovery, 28, 100223. https://doi.org/10.1016/j.medidd.2025.100223
36. Payne, D. J., Gwynn, M. N., Holmes, D. J., & Pompliano, D. L. (2007). Drugs for bad bugs: Confronting the challenges of antibacterial discovery. Nature Reviews. Drug Discovery, 6(1), 29–40. https://doi.org/10.1038/nrd2201
37. Peleg, A. Y., Seifert, H., & Paterson, D. L. (2008). Acinetobacter baumannii: Emergence of a Successful Pathogen. Clinical Microbiology Reviews, 21(3), 538–582. https://doi.org/10.1128/CMR.00058-07
38. Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025). The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review. Antibiotics, 14(2), 134. https://doi.org/10.3390/antibiotics14020134
39. Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019). Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites, 9(4), 76. https://doi.org/10.3390/metabo9040076
40. Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013). Estimation of the size of drug-like chemical space based on GDB-17 data. Journal of Computer-Aided Molecular Design, 27(8), 675–679. https://doi.org/10.1007/s10822-013-9672-4
41. Popa, S. L., Pop, C., Dita, M. O., Brata, V. D., Bolchis, R., Czako, Z., Saadani, M. M., Ismaiel, A., Dumitrascu, D. I., Grad, S., David, L., Cismaru, G., & Padureanu, A. M. (2022). Deep Learning and Antibiotic Resistance. Antibiotics, 11(11), 1674. https://doi.org/10.3390/antibiotics11111674
42. Popova, M., Isayev, O., & Tropsha, A. (2018). Deep reinforcement learning for de novo drug design. Science Advances, 4(7), eaap7885. https://doi.org/10.1126/sciadv.aap7885
43. Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2018). DeepSynergy: Predicting anti-cancer drug synergy with Deep Learning. Bioinformatics (Oxford, England), 34(9), 1538–1546. https://doi.org/10.1093/bioinformatics/btx806
44. Price, R. (2016). O’Neill report on antimicrobial resistance: Funding for antimicrobial specialists should be improved. European Journal of Hospital Pharmacy, 23(4), 245–247. https://doi.org/10.1136/ejhpharm-2016-001013
45. Qiao, D., Li, H., Zhang, X., Chen, X., Zhang, J., Zou, J., Zhao, D., Zhu, W., Qian, X., & Li, H. (2025). The Convergence of Artificial Intelligence and Microfluidics in Drug Research and Development. Engineering. https://doi.org/10.1016/j.eng.2025.07.025
46. Rifaioglu, A. S., Atas, H., Martin, M. J., Cetin-Atalay, R., Atalay, V., & Doğan, T. (2019). Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Briefings in Bioinformatics, 20(5), 1878–1912. https://doi.org/10.1093/bib/bby061
47. Rossiter, S. E., Fletcher, M. H., & Wuest, W. M. (2017). Natural Products as Platforms To Overcome Antibiotic Resistance. Chemical Reviews, 117(19), 12415–12474. https://doi.org/10.1021/acs.chemrev.7b00283
48. Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science (New York, N.Y.), 361(6400), 360–365. https://doi.org/10.1126/science.aat2663
49. Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024a). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10), 1328. https://doi.org/10.3390/pharmaceutics16101328
50. Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024b). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10), 1328. https://doi.org/10.3390/pharmaceutics16101328
51. Serrano, D. R., Luciano, F. C., Anaya, B. J., Ongoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., Rapti, C., Ruiz, H. K., Rawat, S., Kumar, D., & Lalatsa, A. (2024c). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10), 1328. https://doi.org/10.3390/pharmaceutics16101328
52. Singh, A. (2024). Artificial intelligence for drug repurposing against infectious diseases. Artificial Intelligence Chemistry, 2(2), 100071. https://doi.org/10.1016/j.aichem.2024.100071
53. Singh, A., & Kumar, A. (2025). Bacteriological Profile and Antibiotic Resistance Pattern in Respiratory Tract Infection at a Tertiary Care Hospital. International Journal of Science and Research (IJSR), 1517–1526. https://doi.org/10.21275/SR25930082238
54. Singh, S., Gupta, H., Sharma, P., & Sahi, S. (2024). Advances in Artificial Intelligence (AI)-assisted approaches in drug screening. Artificial Intelligence Chemistry, 2(1), 100039. https://doi.org/10.1016/j.aichem.2023.100039
55. So, A. D., & Shah, T. A. (2014). New business models for antibiotic innovation. Upsala Journal of Medical Sciences, 119(2), 176–180. https://doi.org/10.3109/03009734.2014.898717
56. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell, 180(4), 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021
57. Tang, K. W. K., Millar, B. C., & Moore, J. E. (2023). Antimicrobial Resistance (AMR). British Journal of Biomedical Science, 80, 11387. https://doi.org/10.3389/bjbs.2023.11387
58. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews. Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
59. Wang, X., Hripcsak, G., Markatou, M., & Friedman, C. (2009). Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study. Journal of the American Medical Informatics Association : JAMIA, 16(3), 328–337. https://doi.org/10.1197/jamia.M3028
60. Wei, C.-H., Peng, Y., Leaman, R., Davis, A. P., Mattingly, C. J., Li, J., Wiegers, T. C., & Lu, Z. (2016). Assessing the state of the art in biomedical relation extraction: Overview of the BioCreative V chemical-disease relation (CDR) task. Database: The Journal of Biological Databases and Curation, 2016, baw032. https://doi.org/10.1093/database/baw032
61. Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digital Health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
62. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
63. Withers, C. A., Rufai, A. M., Venkatesan, A., Tirunagari, S., Lobentanzer, S., Harrison, M., & Zdrazil, B. (2025). Natural language processing in drug discovery: Bridging the gap between text and therapeutics with artificial intelligence. Expert Opinion on Drug Discovery, 20(6), 765–783. https://doi.org/10.1080/17460441.2025.2490835
64. Yu, W., Shen, P., Luo, Q., Xiong, L., & Xiao, Y. (2022). Efficacy and safety of novel carbapenem–β-lactamase inhibitor combinations: Results from phase II and III trials. Frontiers in Cellular and Infection Microbiology, 12, 925662. https://doi.org/10.3389/fcimb.2022.925662
65. Zhang, J., Li, H., Zhang, Y., Huang, J., Ren, L., Zhang, C., Zou, Q., & Zhang, Y. (2025). Computational toxicology in drug discovery: Applications of artificial intelligence in ADMET and toxicity prediction. Briefings in Bioinformatics, 26(5), bbaf533. https://doi.org/10.1093/bib/bbaf533
66. Zhang, Y., Li, J., Lin, S., Zhao, J., Xiong, Y., & Wei, D.-Q. (2024). An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model. Journal of Cheminformatics, 16, 67. https://doi.org/10.1186/s13321-024-00862-9