Identification of Antibacterial Hits Associated with Penicillin-Binding Protein 2 in

Purpose: Early-stage antibacterial candidate selection requires balancing antibacterial activity with broader developability-related properties. This study developed and applied a Fivefold Maximum Drug-Likeness strategy (5F-MDL) for prioritizing antibacterial candidates against Methods: An ensemble of deep learning models generated a 33-dimensional property spectrum covering physicochemical, pharmacokinetic, efficacy-related, safety, and stability endpoints. Approximately 16 million commercial molecules were screened, and fifteen candidates were experimentally evaluated by disk diffusion and broth microdilution. Molecular docking, molecular dynamics simulations, and a Bocillin-FL competition assay examined potential PBP2-associated interactions. Results: The fifteen prioritized candidates showed high property-spectrum similarity to reference antibiotics, with [Formula: see text] scores ranging from 0.929 to 0.971. Broth microdilution identified several molecules with measurable antibacterial activity, among which M2 showed the most balanced overall profile, including an MIC of 25.6 µg/mL and the largest inhibition zone among the candidates. Docking in the 549 Å Conclusion: The 5F-MDL workflow provides a multidimensional property-spectrum-based approach for early-stage antibacterial candidate prioritization. M2 was identified as a preliminary lead-like hit, although its mechanism, safety profile, and broader applicability require further validation.