Plant-derived compounds and artificial intelligence: Outsmarting microbial pathogens and antimicrobial resistance

Area of Science:

• Phytochemistry
• Pharmacology
• Computational Chemistry

Background:

• Antimicrobial resistance (AMR) is a growing global health threat, demanding novel therapeutic strategies.
• Plant-derived secondary metabolites (PSMs) are recognized for their potent antimicrobial properties.
• Exploring natural products is crucial for developing new treatments against resistant pathogens.

Purpose of the Study:

• To comprehensively review plant-derived antimicrobials, covering extraction, biosynthesis, purification, mechanisms, formulation, and preclinical applications.
• To highlight the integration of artificial intelligence (AI) in advancing phytochemistry for AMR research.
• To examine current research on plant-derived compounds as potential antimicrobial agents.

Main Methods:

• Extensive literature search conducted across Google Scholar, Web of Science, PubMed, and Scopus.
• Analysis of diverse categories of plant-derived antimicrobials and their properties.
• Review of AI applications, including machine learning and deep learning, in drug discovery.

Main Results:

• AI tools like BioGPT and SwissADME accelerate drug target selection and toxicity prediction, overcoming limitations of conventional drug development.
• Plant-derived secondary metabolites exhibit broad-spectrum activity against bacterial and fungal pathogens.
• PSMs interfere with quorum sensing, biofilm formation, damage microbial structures, and induce oxidative stress.

Conclusions:

• Plant-derived compounds are a viable alternative to conventional antimicrobials.
• AI significantly enhances the efficiency and reduces the cost of discovering and developing new antimicrobial agents from plants.
• Further research into phytochemistry holds promise for novel antimicrobial drug development to combat AMR.