This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery and offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens.
Abstract
Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identification of conserved domains and catalytic sites. These computational approaches bridge the gap between genomic data and biological function, accelerating drug discovery and guiding design of new antimicrobial bioactive compounds. Despite notable advances, several challenges have persisted regarding experimental validation and genomic variability, revealing an opportunity to integrate artificial intelligence-driven modeling with bioinformatics as a transformative method for better understanding resistance mechanisms and prioritizing novel therapeutic targets. This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery. Its relevance stems from the validation of computational approaches that transcend the constraints of conventional lab-based biology, allowing for the pinpoint identification of catalytic sites in resistant strains. By outlining the current landscape and validation hurdles, this study offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens. This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery. Its relevance stems from the validation of computational approaches that transcend the constraints of conventional lab-based biology, allowing for the pinpoint identification of catalytic sites in resistant strains. By outlining the current landscape and validation hurdles, this study offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens.
A snapshot of AI-driven technologies for AMP design is provided and two modes of AI-driven technologies for AMP design are surveyed, one concentrated on identifying whether current data possess antimicrobial activity and the other on generating AMP candidates with potential therapeutic properties (generation-oriented).
Yong-Qiang Liu, Jie Hu, Ning Zhang et al.· Synthetic and Systems Biotec...· 0 citations
It is concluded that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.
Hanshal Inagala· Journal of Pharmaceutical Re...· 0 citations
The rapid emergence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) has significantly reduced the effectiveness of conventional therapeutic regimens necessitating the discovery of novel drug targets and inhibitors. Recent bioinformatics-driven approaches for identifying putative inhibitors targeting essential mycobacterial proteins are comprehensively reviewed. Unlike previous reviews that tend to focus either on drug resistance mechanisms or drug discovery using computational approaches separately, this review integrates both aspects by linking genetic mutations associated with drug resistance and advanced computational approaches for anti-TB drug discovery. Integrative computational strategies including subtractive genomics, molecular docking, molecular dynamics simulations and machine learning-based prioritisation are emphasised. These approaches enable the identification of pathogen specific targets with minimal homology with the host proteins. This review further highlights the importance of natural products, peptides and drug repurposing strategies in targeting MDR-TB. Computational pipelines have shown the potential to greatly speed up early-stage drug discovery while lowering related costs and time, despite the challenges. The benefits of combining multi-omics data with artificial intelligence to create strain-specific treatment approaches are further demonstrated by case-based insights. Furthermore, this review highlights the current challenges in translating computational predictions into experimental and clinical validation while providing future directions including AI/ML based drug discovery, network pharmacology, host-directed therapies, and personalized medicine. Overall, this review underscores the critical role of bioinformatics in addressing the global burden of MDR-TB and highlights its transformative role in guiding next-generation anti-TB drug development. Not applicable.
Elizabeth Annie George, Mahima Senthilkumar, Kavitha Thirupugazh et al.· Beni-Suef University Journal...· 0 citations
Antimicrobial resistance has intensified the need for new chemical matter and new mechanisms of action, yet antibacterial discovery remains unusually vulnerable to attrition between a convincing molecular hypothesis and clinically relevant whole-cell activity. This critical narrative review evaluates target-based and phenotypic screening as complementary discovery architectures rather than competing doctrines, with emphasis on small-molecule antibacterial discovery and transferable lessons for antimicrobial research. Literature published from 1 January 2000 to 28 June 2026 was identified through PubMed, PubMed Central, OpenAlex, DOAJ, Semantic Scholar and Google Scholar, supplemented by citation chaining and verification against primary bibliographic records. The evidence indicates that target-based screening is strongest when target vulnerability, chemical tractability and intracellular exposure are demonstrated early, but genomic essentiality alone is an unreliable proxy for pharmacological susceptibility. Phenotypic screening naturally selects for cellular access and functional consequence, yet its apparent physiological realism can be undermined by artificial culture conditions, rediscovery of known mechanisms, non-specific stress phenotypes and protracted target deconvolution. The most productive emerging approaches therefore collapse the traditional divide: genetically sensitised strains and CRISPR interference connect targets to cellular phenotypes; pathway-directed reporters retain whole-cell permeability filters while enriching for desired biology; bacterial cytological profiling, metabolomics and thermal proteome profiling accelerate mechanism resolution; and direct accumulation measurements make permeability and efflux explicit design variables. Machine-learning approaches expand searchable chemical space, but their value depends on experimentally grounded phenotypic labels and subsequent mechanistic validation. Across these strategies, the central determinant of progress is not whether a campaign begins with a target or a phenotype, but whether it rapidly establishes a coherent chain of evidence linking target vulnerability, compound exposure, target engagement, antibacterial effect, resistance liability and disease-relevant performance. Future discovery programmes should be designed around this convergence from the outset.
I. Aliyu, Zephaniah Isaiah, Samson Tesfaye Gebre et al.· South Asian Journal of Resea...· 0 citations
Antimicrobial resistance (AMR) represents one of the most pressing threats to global public health, undermining the effectiveness of modern antimicrobial therapy and challenging decades of medical progress. This comprehensive review examines the transition from broad-spectrum empirical therapy toward precision medicine as an integrated framework for improving antimicrobial use and combating AMR. Precision medicine seeks to tailor treatment decisions by combining pathogen-specific genomic and resistance data with relevant host characteristics to optimize therapy while limiting unnecessary antimicrobial exposure and the selective pressures that drive resistance. The review synthesizes advances reported from 2020, highlighting established and emerging approaches including rapid molecular diagnostics, next-generation sequencing, CRISPR-based detection, machine learning (ML)-assisted decision support, precision dosing, and targeted therapeutics such as bacteriophage therapy, antimicrobial peptides, and bacterial proteolysis-targeting chimeras. Rather than functioning as isolated technologies, these approaches achieve their greatest clinical value when integrated within antimicrobial stewardship programs and a One Health framework that recognizes the interconnected human, animal, and environmental drivers of resistance. Despite considerable progress, important challenges remain, including equitable access to advanced technologies, interpretation of increasingly complex datasets, workforce and infrastructure limitations, and evolving regulatory pathways for novel diagnostics and therapeutics. This review concludes that while precision medicine is not a standalone solution, its successful implementation will depend on coordinated integration of diagnostics, host factors, computational tools, pharmacological optimization, and stewardship strategies to improve patient outcomes while preserving the long-term effectiveness of existing antimicrobials.
Lamarana Jallow, Henry Hodosika, Ousman Bajinka· Respiratory Medicine· 0 citations
The global health crisis of antimicrobial resistance necessitates the discovery of new antibacterial agents. Underexplored marine microbiomes, particularly from the biodiverse Indian coast, represent a rich potential source of antimicrobial peptides (AMPs). Targeting the urgent threat of multidrug-resistant ESKAPE pathogens, the present study aimed to computationally identify novel, membrane-active AMPs from these unique metagenomic datasets, with a focus on inhibiting Gram-negative bacteria. In this study, we computationally mined Indian marine high-resolution shotgun metagenomic datasets through quality filtering, de novo assembly, and small open reading frame prediction. An ensemble of six machine learning-based AMP prediction tools identified over 51,000 high-confidence candidate AMPs. Subsequent filtering based on physicochemical properties and AlphaFold3-predicted structures prioritized ten peptides with favourable membrane-active characteristics. Two lead candidates, c_AMP_1 and c_AMP_2, were subjected to all-atom molecular dynamics simulations within Gram-negative membrane mimetic models of Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae. Our simulations indicated distinct membrane interaction modes: c_AMP_1 adopted a stable, surface-associated α-helical orientation, while c_AMP_2 displayed a more flexible, membrane-inserting orientation in the simulations. Analysis of the MD simulations revealed distinct predicted peptide-membrane interaction profiles, characterized by specific hydrogen bonding patterns, peptide tilt angles, and membrane thinning, which collectively suggest differing biophysical interaction modes. Taken together, our work suggests the Indian marine microbiome as a promising reservoir for novel AMP candidates and suggests that an integrated computational pipeline – combining machine learning, structural biology, and biophysical simulation – may help prioritize candidate peptides for future experimental validation against critical pathogens.
Sreelakshmi K V, Nasri Thaha, B. Dehury· PLoS ONE· 0 citations
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