Systems Biology, Multi-Omics, and Artificial Intelligence Paradigms in Bacterial Mutational Resistance and De Novo Antibacterial Drug Design
DOI:
https://doi.org/10.55578/amsr.2607.012Keywords:
Artificial Intelligence (AI), Machine Learning (ML), Antimicrobial Resistance (AMR), Multidrug-Resistant (MDR), Drug DesignAbstract
Antimicrobial resistance (AMR) represents one of the most critical global threats to public health; it was associated with an estimated 4.95 million deaths in 2019 [1] and is projected to claim up to 10 million lives annually by 2050 if no effective interventions are implemented [2]. The rapid dissemination of multidrug-resistant (MDR) bacterial strains continues to render first-line and last-resort antibiotics ineffective, outpacing traditional drug discovery pipelines [3]. Historically, the development of new antibacterial agents relied on modifying existing chemical classes, a strategy that is increasingly vulnerable to rapid selection of resistance under clinical pressure [4]. To overcome this bottleneck, the modern research paradigm is shifting from single-molecule investigations to systems-level analyses [5]. The convergence of high-throughput multi-omics profiling spanning genomics, transcriptomics, proteomics, and metabolomics with advanced artificial intelligence (AI) and machine learning (ML) architectures offers a powerful framework for deciphering the complex molecular underpinnings of resistance and accelerating target-directed drug discovery [6].
References
[1] Murray, C. J., Ikuta, K. S., Sharara, F., Swetschinski, L., Aguilar, G. R., Gray, A., … Naghavi, M. (2022). Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet, *399*(10325), 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0
[2] Naghavi, M., Vollset, S. E., Ikuta, K. S., Swetschinski, L. R., Gray, A. P., Wool, E. E., … Murray, C. J. (2024). Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. The Lancet, *404*(10459), 1199–1226. https://doi.org/10.1016/S0140-6736(24)01867-1
[3] Hossain, E., & Yousefi, N. (2026). Computational paradigms for antimicrobial resistance prediction: Integrating multi-omics, structural modeling, and foundation artificial intelligence systems. Briefings in Bioinformatics, *27*(3), bbag219. https://doi.org/10.1093/bib/bbag219
[4] Yow, H.-Y., Govindaraju, K., Lim, A. H., & Abdul Rahim, N. (2022). Optimizing antimicrobial therapy by integrating multi-omics with pharmacokinetic/pharmacodynamic models and precision dosing. Frontiers in Pharmacology, *13*, 915355. https://doi.org/10.3389/fphar.2022.915355
[5] Shen, Y., Zhang, P., Luo, J., Chen, S., Gu, S., Lin, Z., … Yang, L. (2026). Artificial intelligence drives advances in multi-omics analysis and precision medicine for sepsis. Biomedicines, *14*(2), 261. https://doi.org/10.3390/biomedicines14020261
[6] Francine, P. (2022). Systems biology: New insight into antibiotic resistance. Microorganisms, *10*(12), 2362. https://doi.org/10.3390/microorganisms10122362
[7] O'Neill, J. (2014). Antimicrobial resistance: Tackling a crisis for the health and wealth of nations. Review on Antimicrobial Resistance.
[8] Fernandez, G. (2022). Turning the juggernaut [Editorial]. The Lancet Infectious Diseases, *22*(5), E75–E76. https://doi.org/10.1016/S1473-3099(22)00254-3
[9] de Kraker, M. E., Stewardson, A. J., & Harbarth, S. (2016). Will 10 million people die a year due to antimicrobial resistance by 2050? PLoS Medicine, *13*(11), e1002184. https://doi.org/10.1371/journal.pmed.1002184
[10] Pulingam, T., Parumasivam, T., Gazzali, A. M., Sulaiman, A. M., Chee, J. Y., Lakshmanan, M., … Sudesh, K. (2022). Antimicrobial resistance: Prevalence, economic burden, mechanisms of resistance and strategies to overcome. European Journal of Pharmaceutical Sciences, *170*, 106103. https://doi.org/10.1016/j.ejps.2021.106103
[11] Lewis, K. (2020). The science of antibiotic discovery. Cell, *181*(1), 29–45. https://doi.org/10.1016/j.cell.2020.02.056
[12] Ribeiro da Cunha, B., Fonseca, L. P., & Calado, C. R. (2019). Antibiotic discovery: Where have we come from, where do we go? Antibiotics, *8*(2), 45. https://doi.org/10.3390/antibiotics8020045
[13] Lewis, K. (2013). Platforms for antibiotic discovery. Nature Reviews Drug Discovery, *12*(5), 371–387. https://doi.org/10.1038/nrd3975
[14] Michael, C. A., Dominey-Howes, D., & Labbate, M. (2014). The antimicrobial resistance crisis: Causes, consequences, and management. Frontiers in Public Health, *2*, 145. https://doi.org/10.3389/fpubh.2014.00145
[15] Salam, M. A., Al-Amin, M. Y., Salam, M. T., Pawar, J. S., Akhter, N., Rabaan, A. A., & Alqumber, M. A. (2023). Antimicrobial resistance: A growing serious threat for global public health. Healthcare, *11*(13), 1946. https://doi.org/10.3390/healthcare11131946
[16] Makabenta, J. M. V., Nabawy, A., Li, C.-H., Schmidt-Malan, S., Patel, R., & Rotello, V. M. (2021). Nanomaterial-based therapeutics for antibiotic-resistant bacterial infections. Nature Reviews Microbiology, *19*(1), 23–36. https://doi.org/10.1038/s41579-020-0420-3
[17] Hetta, H. F., Ramadan, Y. N., Al-Harbi, A. I., Ahmed, E. A., Battah, B., Abd Ellah, N. H., … El-Saber Batiha, G. (2023). Nanotechnology as a promising approach to combat multidrug resistant bacteria: A comprehensive review and future perspectives. Biomedicines, *11*(2), 413. https://doi.org/10.3390/biomedicines11020413
[18] Miranda, R. R., Parthasarathy, A., & Hudson, A. O. (2022). Exploration of chemical biology approaches to facilitate the discovery and development of novel antibiotics. Frontiers in Tropical Diseases, *3*, 845469. https://doi.org/10.3389/fitd.2022.845469
[19] Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., … Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, *180*(4), 688–702. https://doi.org/10.1016/j.cell.2020.01.021
[20] Sakenova, N., Cacace, E., Orakov, A., Huber, F., Varik, V., Kritikos, G., … Typas, A. (2025). Systematic mapping of antibiotic cross-resistance and collateral sensitivity with chemical genetics. Nature Microbiology, *10*(1), 202–216.
[21] Maura, D., Ballok, A. E., & Rahme, L. G. (2016). Considerations and caveats in anti-virulence drug development. Current Opinion in Microbiology, *33*, 41–46. https://doi.org/10.1016/j.mib.2016.07.015
[22] Zampieri, M., Zimmermann, M., Claassen, M., & Sauer, U. (2017). Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations. Cell Reports, *19*(6), 1214–1228. https://doi.org/10.1016/j.celrep.2017.04.028
[23] Prasad, N. K., Seiple, I. B., Cirz, R. T., & Rosenberg, O. S. (2022). Leaks in the pipeline: A failure analysis of gram-negative antibiotic development from 2010 to 2020. Antimicrobial Agents and Chemotherapy, *66*(5), e00054-22. https://doi.org/10.1128/aac.00054-22
[24] Bakkeren, E., Diard, M., & Hardt, W.-D. (2020). Evolutionary causes and consequences of bacterial antibiotic persistence. Nature Reviews Microbiology, *18*(9), 479–490. https://doi.org/10.1038/s41579-020-0361-x
[25] Wei, Y., Palacios Araya, D., & Palmer, K. L. (2024). Enterococcus faecium: Evolution, adaptation, pathogenesis and emerging therapeutics. Nature Reviews Microbiology, *22*(11), 705–721. https://doi.org/10.1038/s41579-024-01056-2
[26] Farha, M. A., French, S., & Brown, E. D. (2021). Systems-level chemical biology to accelerate antibiotic drug discovery. Accounts of Chemical Research, *54*(8), 1909–1920. https://doi.org/10.1021/acs.accounts.0c00771
[27] Roemer, T., & Boone, C. (2013). Systems-level antimicrobial drug and drug synergy discovery. Nature Chemical Biology, *9*(4), 222–231. https://doi.org/10.1038/nchembio.1210
[28] Mujchariyakul, W., Walsh, C. J., Giulieri, S., Cramond, C., Lê Cao, K.-A., Stinear, T. P., … Monk, I. R. (2026). Integrated multi-omics reveals coordinated Staphylococcus aureus metabolic, iron transport, and stress responses to human serum. mSystems, *11*(3), e01183-25. https://doi.org/10.1128/msystems.01183-25
[29] Cloots, L., & Marchal, K. (2011). Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria. Current Opinion in Microbiology, *14*(5), 599–607. https://doi.org/10.1016/j.mib.2011.07.022
[30] Typas, A., & Sourjik, V. (2015). Bacterial protein networks: Properties and functions. Nature Reviews Microbiology, *13*(9), 559–572. https://doi.org/10.1038/nrmicro3483
[31] Klosik, D. F., Grimbs, A., Bornholdt, S., & Hütt, M.-T. (2017). The interdependent network of gene regulation and metabolism is robust where it needs to be. Nature Communications, *8*(1), 534. https://doi.org/10.1038/s41467-017-00493-5
[32] Sarker, M., Talcott, C., & Galande, A. K. (2013). In silico systems biology approaches for the identification of antimicrobial targets. In In silico Models for Drug Discovery (pp. 13–30). Humana Press. https://doi.org/10.1007/978-1-62703-342-8_2
[33] Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019). Current status and applications of genome-scale metabolic models. Genome Biology, *20*(1), 121. https://doi.org/10.1186/s13059-019-1730-3
[34] Porreca, V., Barbagallo, C., Corbella, E., Peres, M., Stella, M., Mignogna, G., … Ferro, A. (2024). Unveil intrahepatic cholangiocarcinoma heterogeneity through the lens of omics and multi-omics approaches. Cancers, *16*(16), 2889. https://doi.org/10.3390/cancers16162889
[35] Sakaguchi, T., Irifune, Y., Kamada, R., & Sakaguchi, K. (2025). Bacterial systematic genetics and integrated multi-omics: Beyond static genomics toward predictive models. International Journal of Molecular Sciences, *26*(19), 9326. https://doi.org/10.3390/ijms26199326
[36] Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L., & Palsson, B. Ø. (2009). Reconstruction of biochemical networks in microorganisms. Nature Reviews Microbiology, *7*(2), 129–143. https://doi.org/10.1038/nrmicro2089
[37] Young, A. T., Carette, X., Helmel, M., Steen, H., Husson, R. N., Quackenbush, J., … Mattila, J. T. (2021). Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis. NPJ Systems Biology and Applications, *7*(1), 8. https://doi.org/10.1038/s41540-020-00155-9
[38] Acierno, C., Barletta, F., Nevola, R., Rinaldi, L., Sasso, F. C., Adinolfi, L. E., … Marrone, A. (2025). Metabolic rewiring of bacterial pathogens in response to antibiotic pressure—a molecular perspective. International Journal of Molecular Sciences, *26*(12), 5574. https://doi.org/10.3390/ijms26125574
[39] Vemuri, G. N., & Aristidou, A. A. (2005). Metabolic engineering in the-omics era: Elucidating and modulating regulatory networks. Microbiology and Molecular Biology Reviews, *69*(2), 197–216. https://doi.org/10.1128/MMBR.69.2.197-216.2005
[40] Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, *14*, 1177932219899051. https://doi.org/10.1177/1177932219899051
[41] Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019). Integrated omics: Tools, advances and future approaches. Journal of Molecular Endocrinology, *62*(1), R21–R45. https://doi.org/10.1530/JME-18-0122
[42] Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in Bioinformatics, *18*(5), 851–869. https://doi.org/10.1093/bib/bbw068
[43] Ma, Y., Yao, A., Chen, X., Wang, L., Ma, C., Xi, X., … Wang, X. (2021). Generation of truncated derivatives through in silico enzymatic digest of peptide GV30 target MRSA both in vitro and in vivo. Computational and Structural Biotechnology Journal, *19*, 4984–4996. https://doi.org/10.1016/j.csbj.2021.08.032
[44] Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., … Taylor, S. (2021). Utilizing graph machine learning within drug discovery and development. Briefings in Bioinformatics, *22*(6), bbab159. https://doi.org/10.1093/bib/bbab159
[45] Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., … Zitnick, C. L. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, *118*(15), e2016239118. https://doi.org/10.1073/pnas.2016239118
[46] Vaidyanathan, R., Kalishwaralal, K., Gopalram, S., & Gurunathan, S. (2009). Nanosilver—The burgeoning therapeutic molecule and its green synthesis [Retracted article]. Biotechnology Advances, *27*(6), 924–937. https://doi.org/10.1016/j.biotechadv.2009.06.002
[47] Chavali, A. K., D'Auria, K. M., Hewlett, E. L., Pearson, R. D., & Papin, J. A. (2012). A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends in Microbiology, *20*(3), 113–123. https://doi.org/10.1016/j.tim.2011.12.002
[48] Yuan, Y., & Bar-Joseph, Z. (2019). Deep learning for inferring gene relationships from single-cell expression data. Proceedings of the National Academy of Sciences, *116*(52), 27151–27158. https://doi.org/10.1073/pnas.1914536116
[49] Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, *18*(1), 83. https://doi.org/10.1186/s13059-017-1215-1
[50] Nembrini, S., König, I. R., & Wright, M. N. (2018). The revival of the Gini importance? Bioinformatics, *34*(21), 3711–3718. https://doi.org/10.1093/bioinformatics/bty373
[51] Arango-Argoty, G., Garner, E., Pruden, A., Heath, L. S., Vikesland, P., & Zhang, L. (2018). DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome, *6*(1), 23. https://doi.org/10.1186/s40168-018-0401-z
[52] Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023). Scaling deep learning for materials discovery. Nature, *624*(7990), 80–85. https://doi.org/10.1038/s41586-023-06735-9
[53] Munita, J. M., & Arias, C. A. (2016). Mechanisms of antibiotic resistance. In Virulence Mechanisms of Bacterial Pathogens (pp. 481–511). American Society of Microbiology. https://doi.org/10.1128/9781555819286.ch17
[54] Misra, R. (2026). Mutation-based mechanisms of antibiotic resistance. Microbiology and Molecular Biology Reviews, e00101-25. https://doi.org/10.1128/mmbr.00101-25
[55] Al-Wahaibi, L. H., Alzahrani, H. A., Bräse, S., Youssif, B. G., & El-Sheref, E. M. (2026). Design and synthesis of novel quinolin-2-ones as dual inhibitors of DNA gyrase and topoisomerase IV possessing antibacterial and apoptotic antiproliferative effects. Journal of Molecular Structure, 146525. https://doi.org/10.1016/j.molstruc.2026.146525
[56] Nusrat, S., Aliyu, M., & Zohora, F. T. (2025). Mechanisms of antimicrobial resistance: From genetic evolution to clinical manifestations. AIMS Microbiology, *11*(4), 1007.
[57] Su, M., Satola, S. W., & Read, T. D. (2019). Genome-based prediction of bacterial antibiotic resistance. Journal of Clinical Microbiology, *57*(3), e01405-18. https://doi.org/10.1128/JCM.01405-18
[58] Nguyen, M., Long, S. W., McDermott, P. F., Olsen, R. J., Olson, R., Stevens, R. L., … Musser, J. M. (2019). Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. Journal of Clinical Microbiology, *57*(2), e01260-18. https://doi.org/10.1128/JCM.01260-18
[59] Yang, Y., Niehaus, K. E., Walker, T. M., Iqbal, Z., Walker, A. S., Wilson, D. J., … Clifton, D. A. (2018). Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, *34*(10), 1666–1671. https://doi.org/10.1093/bioinformatics/btx708
[60] Van Dijk, E. L., Jaszczyszyn, Y., Naquin, D., & Thermes, C. (2018). The third revolution in sequencing technology. Trends in Genetics, *34*(9), 666–681. https://doi.org/10.1016/j.tig.2018.05.006
[61] Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., … Ellinor, P. T. (2023). Transfer learning enables predictions in network biology. Nature, *618*(7965), 616–624. https://doi.org/10.1038/s41586-023-06239-6
[62] Ji, Y., Zhou, Z., Liu, H., & Davuluri, R. V. (2021). DNABERT: Pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinformatics, *37*(15), 2112–2120. https://doi.org/10.1093/bioinformatics/btab083
[63] Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A. H., Oteri, F., … Bencherif, T. (2025). Nucleotide transformer: Building and evaluating robust foundation models for human genomics. Nature Methods, *22*(2), 287–297.
[64] Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint, arXiv:2108.07258. https://arxiv.org/abs/2108.07258
[65] Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K.-R. (2019). Explainable AI: Interpreting, explaining and visualizing deep learning. Springer Nature. https://doi.org/10.1007/978-3-030-28954-6
[66] Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning (pp. 3319–3328). PMLR.
[67] Ghosh, A., Brenner, E. P., Vang, C. K., Wolfe, E. P., Boyer, E. A., Lesiyon, R. L., … Allen, H. K. (2025). From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens. bioRxiv. https://doi.org/10.1101/2025.07.03.663053
[68] Hasan, M. I., Smyth, D. S., & Teufel, A. I. (2025). Network analysis of antimicrobial resistance in Staphylococcus aureus: Characterization of hub genes and their functional implications. NAR Genomics and Bioinformatics, *7*(4), lqaf147. https://doi.org/10.1093/nargab/lqaf147
[69] Ren, Y., Bai, H., Wang, J., Yang, Y., & Wang, Y. (2026). Deep learning-enabled multi-omics integration: A new frontier in precise drug target discovery. Biology, *15*(5), 410. https://doi.org/10.3390/biology15050410
[70] Vora, N., Shah, S., Patel, P., & Shah, M. (2025). Artificial intelligence and multi-omics in drug discovery: A deep learning-powered revolution. Cure & Care, 100011.
[71] Fang, X., Lloyd, C. J., & Palsson, B. O. (2020). Reconstructing organisms in silico: Genome-scale models and their emerging applications. Nature Reviews Microbiology, *18*(12), 731–743. https://doi.org/10.1038/s41579-020-00463-9
[72] Shahreen, N., Osinuga, A., Malla, S., Razmpour, T., Tabibian, M., & Saha, R. (2026). Multi-omics integration in genome-scale metabolic models: A review of constraint-based approaches. Molecular Omics, *22*(2), aaiag005.
[73] Kula, C., Erguven, I., Ozcelik, B., Gulfidan, G., & Arga, K. Y. (2026). Transcriptome-based machine learning models to predict antimicrobial resistance in Pseudomonas aeruginosa. OMICS: A Journal of Integrative Biology, *30*(2), 94–104.
[74] Sanapalli, B. K. R., Palit, S., Deshpande, A., Tokala, R., Sigalapalli, D. K., & Sanapalli, V. (2026). Artificial intelligence and the discovery of antibiotics: Reinventing with opportunities, challenges, and clinical translation. Antibiotics, *15*(2), 233. https://doi.org/10.3390/antibiotics15020233
[75] Wang, L., Campino, S., Clark, T. G., & Phelan, J. E. (2025). Long short-term memory-based deep learning model for the discovery of antimicrobial peptides targeting Mycobacterium tuberculosis. Bioinformatics Advances, *5*(1), vbaf274. https://doi.org/10.1093/bioadv/vbaf274
[76] Tuan, D. A., Uyen, P. V. N., Ho, T., & Vo Thien Nhan, N. (2025). Hybrid quorum sensing and machine learning systems for precision gene regulation: Revolutionizing synthetic biology and autonomous therapeutic applications. Frontiers in Microbiology, *16*, 1520227.
[77] Alquaddoomi, F. S., Burke, J. T., Sosinski, L., Mayer, D. A., Brenner, E. P., Chen, S. Z., … Allen, H. K. (2022). MolEvolvR: A web-app for characterizing proteins using molecular evolution and phylogeny. bioRxiv. https://doi.org/10.1101/2022.02.18.461833
[78] Tait, J. R., Anderson, D., Nation, R. L., Creek, D. J., & Landersdorfer, C. B. (2024). Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa. Antimicrobial Agents and Chemotherapy, *68*(4), e01081-23. https://doi.org/10.1128/aac.01081-23
[79] Rashid, M. M., & Selvarajoo, K. (2024). Advancing drug-response prediction using multi-modal and-omics machine learning integration (MOMLIN): A case study on breast cancer clinical data. Briefings in Bioinformatics, *25*(4), bbae300. https://doi.org/10.1093/bib/bbae300
[80] Zhang, Y., Liu, L., Liu, Z., Liu, Q., Ma, L., & Zhang, Z. (2025). Machine learning for integrative multi-omics clustering and feature gene identification. bioRxiv. https://doi.org/10.1101/2025.11.28.691099
[81] Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., … von Mering, C. (2023). The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, *51*(D1), D638–D646. https://doi.org/10.1093/nar/gkac1000
[82] Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., … McArthur, A. G. (2023). CARD 2023: Expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, *51*(D1), D690–D699. https://doi.org/10.1093/nar/gkac989
[83] Feldgarden, M., Brover, V., Gonzalez-Escalona, N., Frye, J. G., Haendiges, J., Haft, D. H., … Klimke, W. (2021). AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Scientific Reports, *11*(1), 12728. https://doi.org/10.1038/s41598-021-91456-0
[84] Bortolaia, V., Kaas, R. S., Ruppe, E., Roberts, M. C., Schwarz, S., Cattoir, V., … Aarestrup, F. M. (2020). ResFinder 4.0 for predictions of phenotypes from genotypes. Journal of Antimicrobial Chemotherapy, *75*(12), 3491–3500. https://doi.org/10.1093/jac/dkaa345
[85] Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., … Barzilay, R. (2019). Analyzing learned molecular representations for property prediction. Journal of Chemical Information and Modeling, *59*(8), 3370–3388. https://doi.org/10.1021/acs.jcim.9b00237
[86] Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017). mixOmics: An R package for 'omics feature selection and multiple data integration. PLoS Computational Biology, *13*(11), e1005752. https://doi.org/10.1371/journal.pcbi.1005752
[87] Merkel, D. (2014). Docker: Lightweight Linux containers for consistent development and deployment. Linux Journal, *239*(2), 2.
[88] Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE, *12*(5), e0177459. https://doi.org/10.1371/journal.pone.0177459
[89] Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, *35*(4), 316–319. https://doi.org/10.1038/nbt.3820
[90] Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., van Dyken, P. C., Tomkins-Tinch, C. H., … Köster, J. (2025). Sustainable data analysis with Snakemake. F1000Research, *10*, 33. https://doi.org/10.12688/f1000research.29032.2
[91] Hussein, M., Creek, D. J., Baker, M., Rao, G. G., Li, J., & Velkov, T. (2025). Metabolomics-driven prediction of antibiotic resistance: A perspective on pre-genetic intervention. npj Antimicrobials and Resistance, *3*(1), 101.
[92] Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., … Zhang, 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
[93] Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022). Artificial intelligence in biological sciences. Life, *12*(9), 1430. https://doi.org/10.3390/life12091430
[94] Timmons, P. B., & Hewage, C. M. (2020). HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Scientific Reports, *10*(1), 10869. https://doi.org/10.1038/s41598-020-67945-5
[95] Theuretzbacher, U., Outterson, K., Engel, A., & Karlén, A. (2020). The global preclinical antibacterial pipeline. Nature Reviews Microbiology, *18*(5), 275–285. https://doi.org/10.1038/s41579-019-0288-6
[96] Mehta, V., Komanduri, A., Bhadouriya, R. S., Mehta, V., Johnson, M. D., Shrestha, P., … Reddy, S. (2025). Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices. npj Digital Medicine, *8*(1), 673.
[97] Yang, H., & Zhao, L. (2025). Clinical trial modernization: Technological, operational, and regulatory advances. Chapman and Hall/CRC.
[98] Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, *3*(1), 17. https://doi.org/10.1038/s41746-019-0221-x
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