Infectious diseases continue to pose unprecedented challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce, and cause damage to host cells, while antibiotic resistance genes (ARGs) enable pathogens to withstand treatments that would otherwise be effective. The concurrent identification of VFs and ARGs is crucial for efficient pathogen surveillance. However, existing tools for predicting VFs or ARGs typically suffer from high false negative rates and limitations in identifying only high-identity genes against known reference VF or ARG databases.