Jul 2026· Cell Host and Microbe· Vol 34 7, pp.
1273-1284
· 1 citation· 94 references
Medicine
TL;DR
The transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation is reviewed.
Abstract
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
Antimicrobial resistance (AMR) has been identified as one of the 21st century’s severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs’ role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.
Veilumuthu Pattapulavar, Sathiyabama Ramanujam, Avanthi Yellampalli et al.· Frontiers in Bioinformatics· 0 citations
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
Antimicrobial resistance (AMR) is currently one of the leading global health threats. The evolution of drug-resistant bacterial pathogens is rapid, and there is a growing number of bacterial pathogens that have developed resistance to multiple antibiotics, and the rate at which new antibiotics are being developed is lagging far behind these two issues. In addition, historical drug discovery processes rely on conducting traditional in vitro-based studies to determine new antibiotics to use in practice. However, this process is becoming increasingly constricted due to high costs of conducting traditional in vitro research, lengthy timeframes to bring products to market, a high attrition rate of research projects in traditional wet laboratory environments, and the need for more effective and efficient ways of developing new drugs. For this reason, Drug discovery continues to evolve from a wet lab-based approach to an in silico (i.e., computational) based approach, which takes advantage of the advances made in various fields, such as bacterial genomics, structural bioinformatics, machine learning (ML), and systems biology to enable researchers to rationally design, discover, and develop new antibiotics to combat drug-resistant pathogens. This review aims to provide a comprehensive and critical overview of contemporary in silico antibiotic discovery strategies and their potential to accelerate the development of novel, resistance-resilient, and clinically relevant antimicrobial agents. It examines genome-informed approaches ranging from genomic data generation, resistome analysis, and computational target identification to structure-based drug design, ligand-based and fragment-based discovery, drug repurposing, and the expanding applications of ML and artificial intelligence (AI) in activity prediction, de novo antibiotic design, and resistance evolution modeling. The review also highlights the importance of in silico ADMET prediction in lead optimization and discusses representative case studies demonstrating successful translation of computational predictions into experimental validation. Overall, the integration of digital-first, data-driven, and genome-guided discovery pipelines with experimental validation offers a powerful framework to overcome current challenges in antibiotic development and represents a promising strategy for addressing the global threat of AMR. Lastly, the review was conducted using a structured literature search across major biomedical and computational databases with emphasis on experimentally validated case studies and translational relevance.
Rohan Gupta, Pallavi Singh, Karthikeyan Ravi et al.· Frontiers in Bioinformatics· 0 citations
Natural products remain a major source of structurally diverse and biologically active small molecules, yet traditional activity-guided discovery is labor-intensive and prone to rediscovery, while untargeted genome mining often lacks efficient prioritization criteria for biosynthetic gene clusters (BGCs). Self-resistance-gene guided discovery has emerged as a powerful strategy to address this limitation. In producing organisms, toxic metabolites are typically accompanied by genetically encoded self-protection mechanisms, such as resistant target homologs, duplicated housekeeping genes, detoxification enzymes, repair systems, or transporters. When co-localized with BGCs, these determinants serve as functional markers for predicting bioactivity and, in some cases, molecular targets prior to compound isolation. Over the past decade, this concept has evolved into a target-directed genome mining framework supported by tools and databases including ARTS, FunARTS, antiSMASH, and MIBiG. This review summarizes the biological basis, workflow, representative advances, and limitations of this strategy. Self-resistance genes can thus be viewed as functional beacons for accelerating bioactive natural product discovery.
Jiaxin Wu, Meng-Xu Qiao, Ya-Yue Ma et al.· Microorganisms· 0 citations
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. 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].
Monochura Saha, Mehra Smriti, Priyanka Kumari· Advances in Medical Sciences...· 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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