Computational Intelligence in Antimicrobial Resistance: From Reactive Treatment to Predictive Drug Discovery and Genomics

Golam Sarwar; Boishakhi Rani Das; Tanvir Hossain; Mohammad Asaduzzaman

doi:10.25163/bioinformatics.6110718

Bioinfo Chem

System biology and Infochemistry | Online ISSN 3071-4826

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Volume 6 Number 1 2024

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Computational Intelligence in Antimicrobial Resistance: From Reactive Treatment to Predictive Drug Discovery and Genomics

Abstract 1. Introduction 2. Methodology 4. Synthesizing Computational Frontiers in Antimicrobial Resistance 5. Limitations 6. Conclusion Author Contributions References

Golam Sarwar ¹, Boishakhi Rani Das ¹, Tanvir Hossain¹, Mohammad Asaduzzaman 1*

+ Author Affiliations

Bioinfo Chem 6 (1) 1-14 https://doi.org/10.25163/bioinformatics.6110718

Submitted: 25 February 2024 Revised: 12 April 2024 Accepted: 20 April 2024 Published: 22 April 2024

Abstract

Antimicrobial resistance (AMR) is increasingly understood not simply as a clinical challenge, but as a shifting and, at times, difficult-to-predict system shaped by microbial evolution, clinical practice, and global health dynamics. While conventional antibiotic strategies remain central, their limitations are becoming more apparent, particularly as resistance mechanisms emerge faster than new therapies can be developed. In this context, computational approaches—especially artificial intelligence (AI), machine learning, and in silico modeling—are beginning to offer alternative ways of interpreting and responding to antimicrobial resistance. This review brings together current evidence on how artificial intelligence and machine learning are being applied across antimicrobial research, from computational drug discovery to genomic prediction and clinical decision support. In several areas, the progress is notable. Machine learning models, particularly those applied to whole-genome sequencing and large clinical datasets, appear capable of identifying resistance patterns earlier and, in some cases, with greater precision than traditional approaches. Similarly, computational drug discovery—including computer-aided design and antimicrobial peptide modeling—suggests a potential reduction in the time required to identify novel therapeutic candidates, though this promise remains uneven across studies. At the same time, the picture is not entirely straightforward. Model performance often depends heavily on dataset quality, scale, and diversity, and issues such as data imbalance, limited interpretability, and challenges in clinical integration continue to shape how these tools are evaluated and, perhaps more importantly, trusted. It becomes increasingly clear that artificial intelligence and machine learning do not replace conventional antimicrobial strategies; rather, they extend them—introducing a more predictive, data-driven perspective on antimicrobial resistance. Taken together, these developments suggest a gradual shift. AMR may be less a problem of isolated pathogens and more a question of patterns, probabilities, and interconnected systems. In that sense, computational approaches—while not definitive solutions—offer a framework that is at once anticipatory, adaptive, and, cautiously, transformative for the future of antimicrobial resistance research.

Keywords: Antimicrobial resistance; Machine learning; Computational drug discovery; Genomic prediction; Antimicrobial peptides

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Computational Intelligence in Antimicrobial Resistance: From Reactive Treatment to Predictive Drug Discovery and Genomics

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