Generative AI and Large Language Models in Microbial Evolution, Resistance Mechanisms, and Antimicrobial Drug Discovery

Generative AI and Large Language Models (LLMs) are transforming microbiology by providing novel insights into microbial evolution, resistance mechanisms, and antimicrobial drug discovery. These advanced machine learning models facilitate the prediction of microbial adaptation and resistance pathways by analyzing complex genomic, transcriptomic, and proteomic data, thus enabling a deeper understanding of evolutionary trajectories. Through the integration of Explainable AI (XAI), these models enhance the interpretability of microbial evolution, including adaptive mutations, horizontal gene transfer, and resistance mechanisms against antibiotics and antiviral agents.

In antimicrobial drug discovery, Generative AI is pioneering the identification and optimization of compounds derived from microbes or targeting microbial pathogens, accelerating the repurposing of existing antimicrobials against multi-drug-resistant bacteria and rapidly mutating viruses. Furthermore, LLMs enhance the prediction of drug-microbe interactions and optimize drug-target binding, thereby contributing to the development of next-generation antimicrobials.

This Research Topic invites interdisciplinary contributions that explore the application of Generative AI and LLMs in:

Predicting microbial resistance mechanisms and evolutionary dynamics,
Discovering and optimizing antimicrobial compounds derived from or targeting microbes,
Enhancing the understanding of host-microbe interactions through AI-driven genomic analysis,
Implementing XAI for transparent and interpretable microbial evolutionary models,
Addressing ethical considerations in AI-driven antimicrobial drug discovery.
By focusing on microbial evolution and antimicrobial strategies, this Research Topic bridges computational biology, microbiology, and artificial intelligence, positioning Generative AI and LLMs as pivotal tools in modern microbiology and drug discovery.