Strategies Used for the Discovery of New Microbial Metabolites with Antibiotic Activity

Abstract

The discovery of new microbial metabolites is essential to combat the alarming rise in antimicrobial resistance and to meet emerging medical needs. This work critically reviews current strategies for identifying antimicrobial compounds, emphasizing the potential of microorganisms as a rich source of bioactive secondary metabolites. This review explores innovative methods, such as investigating extreme environments where adverse conditions favor the emergence of unique metabolites; developing techniques, like the iChip, to cultivate previously uncultivable bacteria; using metagenomics to analyze complex samples that are difficult to isolate; and integrates artificial intelligence to accelerate genomic mining, structural prediction, and drug discovery optimization processes. The importance of overcoming current challenges, such as replicating findings, low research investment, and the lack of adapted collection technologies, is also emphasized. Additionally, this work analyzes the crucial role of bacterial resistance and the necessity of a holistic approach involving new technologies, sustained investment, and interdisciplinary collaboration. This work emphasizes not only the current state of metabolite discovery but also the challenges that must be addressed to ensure a continuous flow of new therapeutic molecules in the coming decades.

Keywords: antibiotic; antimicrobial resistance; artificial intelligence; drug discovery; extremophiles; isolation Chip; metagenomics; microorganisms; natural products; secondary metabolites.

Figure 3

Diagram of the assembled iChip system. It consists of upper and lower covers, semipermeable polycarbonate membranes, and a central polyoxymethylene (POM) plate. Figure was developed in BioRender (https://app.biorender.com/, accessed on 3 July 2025) based on the review by Sherpa et al. [70].

Figure 1

The chemical structures of several recently developed metabolites. (A) Gepotidacin is an antibacterial compound approved by FDA in 2025. (B) Oritavancin, (C) dalbavancin, and (D) eremomycin pyrrolidide are glycopeptides used to treat bacterial infections. (E) Teixobactin is a cyclodipeptide employed for infections produced by MRSA. (F) Avibactam is a penicillin derivative utilized against Gram-negative bacteria. (G) Aureothin is a polyketide antibiotic and antiviral agent.

Figure 2

The chemical structures of some new antibiotics discovered from microorganisms in extreme environments.

Figure 4

The chemical structures of some new antibiotics discovered through the use of diffusion chambers.

Figure 5

FACS-iChip system, in which, before culturing the sample on the iChip, live cells are separated from dead cells using flow cytometry. Figure was created using BioRender (https://app.biorender.com/, accessed on 3 July 2025) with the information provided by Liu et al. [78].

Figure 6

The chemical structures of some bioactive natural products discovered using metagenomic approaches.

Figure 7

Summary diagram of the process for searching for new natural products. The diagram illustrates how artificial intelligence can optimize each level and step of the process. This figure was created using BioRender (https://app.biorender.com/, accessed on 3 July 2025).

Figure 8

Artificial intelligence applications in the discovery of novel natural products. These include (1) metagenomics and metabolomics; (2) biological function prediction; (3) chemical structure elucidation. The figure was created in BioRender (https://app.biorender.com/, accessed on 3 July 2025).

Figure 9

The chemical structures of some new antibiotics discovered with the aid of artificial intelligence.

Figure 10

Advantages and constraints of the strategies described in this revision for the discovery of new microbial metabolites with antibiotic activity.