The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancement...The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancements in AI technology,the next generation of AI is increasingly prevalent in microbiology research.In microbial research,AI is instrumental in the classification and functional annotation of microorganisms.Machine learning algorithms facilitate efficient and accurate categorization of microbial taxa,enabling the identification of functional traits and metabolic pathways within microbial communities.Additionally,AI-driven protein design strategies hold promise for engineering enzymes with enhanced catalytic activities and stabilities.By predicting protein structures,functions,and interactions,AI algorithms enable the rational design of proteins and enzymes tailored for specific applications.AI systems are already present in clinical microbiology laboratories in the form of expert rules used by some automated susceptibility testing and identification systems.In the future,microbiology technologists will rely more heavily on AI for initial screening,allowing them to focus on diagnostic challenges and complex technical interpretations.AI-driven approaches hold immense promise in advancing our understanding of microbial ecosystems,accelerating drug discovery processes,and fostering the development of groundbreaking therapeutic interventions.This review aims to summarize common algorithms in AI and their applications within microbiology and synthetic biology.We provide a comprehensive evaluation of AI’s utility in microbial research,discussing both its advantages and challenges.Finally,we explore future research directions and the bottlenecks faced by AI in the microbial field.展开更多
基金supported by the National Natural Science Foundation Projects of China(No.82350003,No.92049201).
文摘The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancements in AI technology,the next generation of AI is increasingly prevalent in microbiology research.In microbial research,AI is instrumental in the classification and functional annotation of microorganisms.Machine learning algorithms facilitate efficient and accurate categorization of microbial taxa,enabling the identification of functional traits and metabolic pathways within microbial communities.Additionally,AI-driven protein design strategies hold promise for engineering enzymes with enhanced catalytic activities and stabilities.By predicting protein structures,functions,and interactions,AI algorithms enable the rational design of proteins and enzymes tailored for specific applications.AI systems are already present in clinical microbiology laboratories in the form of expert rules used by some automated susceptibility testing and identification systems.In the future,microbiology technologists will rely more heavily on AI for initial screening,allowing them to focus on diagnostic challenges and complex technical interpretations.AI-driven approaches hold immense promise in advancing our understanding of microbial ecosystems,accelerating drug discovery processes,and fostering the development of groundbreaking therapeutic interventions.This review aims to summarize common algorithms in AI and their applications within microbiology and synthetic biology.We provide a comprehensive evaluation of AI’s utility in microbial research,discussing both its advantages and challenges.Finally,we explore future research directions and the bottlenecks faced by AI in the microbial field.
