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The rise of antibiotic resistance has created an urgent need for the discovery of new antibiotic compounds. Streptomycin, the first antibiotic isolated from Streptomyces sp., paved the way for discovering other antibiotics for combating bacterial infections. By exploring the genome-based biosynthetic potential of various Streptomyces species, a vast array of secondary metabolites with potential therapeutic applications can be identified, contributing a transformative impact on the field of medicine. However, conventional screening approaches on novel natural products (NPs) from Streptomyces sp. have entered a bottleneck due to inefficiency. Fortunately, artificial intelligence (AI) and machine learning (ML) models enable rapid exploration and prediction of potential antibiotic compounds, increasing the probability of discovering new antibacterial compounds. AI-driven drug discovery in Streptomyces sp. represents a paradigm shift in the future quest for novel pharmaceutical agents. Various ML models have been developed and applied in different practical applications. Overall, the ML model is trained using input data and generates outcomes based on prediction output. This review discusses the continued potential of Streptomyces sp. as a source of novel NPs, along with the application of ML throughout the NP drug discovery pipeline involving genome mining, biological activities prediction, and optimization compound production in Streptomyces microbial systems. Graphical abstract: The role of machine learning in drug discovery from Streptomyces.

A new next-generation probiotic, Christensenella minuta was first discovered in 2012 from healthy human stool and described under the phylum Firmicutes. C. minuta is a subdominant commensal bacterium with highly heritable properties that exhibits mutual interactions with other heritable microbiomes, and its relative abundance is positively correlated with the lean host phenotype associated with a low BMI index. It has been the subject of numerous studies, owing to its potential health benefits. This article reviews the evidence from various studies of C. minuta interventions using animal models for managing metabolic diseases, such as obesity, inflammatory bowel disease, and type 2 diabetes, characterized by gut microbiota dysbiosis and disruption of host metabolism. Notably, more studies have presented the complex interaction between C. minuta and host metabolism when it comes to metabolic health. Therefore, C. minuta could be a potential candidate for innovative microbiome-based biotherapy via fecal microbiota transplantation or oral administration. However, the detailed underlying mechanism of action requires further investigation.