Artificial Intelligence in Microbiology: Applications in Microbial Data Analysis and Drug Discovery

Artificial intelligence (AI) is revolutionizing microbiology by allowing us to analyze more intricate microbial data in a faster and more effective manner, as well as propel the discovery of new antimicrobial agents. The exponentially growing set of genomic, transcriptomic, proteomic, and metabolomic datasets presents some intricate multi-dimensional data landscapes that necessitate sophisticated statistical methods for a meaningful interpretation. Within this context, AI-powered approaches—particularly machine learning and deep learning—provide powerful new tools to tease out previously hidden relationships between microbial taxa, their functional roles, and host–microbe interactions. These features are particularly useful for microbiome data, where microbial communities are composed of diverse and dynamic populations that often behave in a context-dependent manner. Furthermore, AI plays a growing role in drug discovery processes, helping researchers identify new antimicrobial compounds among millions of potential candidates from chemical libraries and design rationally molecules with higher potency and lower toxicity. AI models trained on structural, physicochemical, and biological data are capable of predicting small molecule and antimicrobial peptide activity, simulating their interactions with microbial targets, and anticipating potential resistance mechanisms. Furthermore, generative AI methods enable de novo molecular design to further develop narrow-spectrum or resistance-resilient antimicrobial agents. However, numerous obstacles persist, such as low data quality, model interpretability, and the essential need for careful experimental validation. In summary, Artificial intelligence is often a game-changer in the microbiome future.