The ability to accurately predict bacterial phenotypes from species names represents a critical intersection of computational biology and practical microbiology. In clinical settings, rapid phenotype prediction can mean the difference between timely, targeted treatment and delayed intervention. When a clinician encounters an unfamiliar bacterial species, immediate access to predicted characteristics—such as Gram staining properties, oxygen requirements, or potential pathogenicity—can guide initial diagnostic and therapeutic decisions while awaiting culture results. Beyond immediate clinical applications, phenotype predictions serve as powerful tools for biological discovery, helping bridge the gap between rapid genomic identification and traditional laboratory characterization. These predictions can prioritize experimental validation efforts, focusing limited laboratory resources on the most promising or clinically relevant discoveries.