Antimicrobial resistance represents a critical global public health challenge, with the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa) and Escherichia coli posing a significant worldwide threat. These pathogens exhibit remarkable adaptability to healthcare environments and possess multidrug-resistant mechanisms, presenting substantial obstacles in the development of novel therapeutic interventions. Ribonucleotide reductases (RNRs) are essential enzymes that catalyze the synthesis of deoxyribonucleotides required for DNA replication and repair. In bacteria, the transcriptional repressor NrdR acts as a universal suppressor of all RNR classes, effectively regulating nucleotide synthesis and homeostasis. Current challenges in targeting bacterial RNRs lie in their redundancy. Blocking one class can upregulate others, allowing bacterial survival. However, stimulating the DNA binding capacity of NrdR halts the transcription of all bacterial RNRs simultaneously without affecting human counterparts, presenting a unique and innovative mechanism to overcome resistance. High-resolution crystal structures of NrdR from E. coli and S. coelicolor provides structural insights into its DNA-bound and inactive conformations. Given NrdR's ubiquity across bacterial genomes, this target holds the potential for broad-spectrum antibacterial therapies, including multidrug-resistant strains with no current treatment options. Modern techniques, such as Computer Aided Drug Design (CADD), have been incorporated into the drug discovery pipeline, and by using these computational approaches researchers can accelerate the identification of promising drug candidates, optimize their properties, reduce development time, costs and its impact of residual chemicals in the environment. Despite the advantages of CADD, the implementation of its methodologies generates a considerable amount of data outcome from the experiments. Given the associated compute application (NAISS 2025/5-301), the present proposal aims to manage the data generated through virtual screening campaigns from that allocation.