Drug-likeness prioritised selection identifies anti-Escherichia coli candidates confirmed by molecular docking, dynamics simulations, and antibacterial assays.

To address the imbalance between antibacterial potency and developability in cephalosporin discovery against Escherichia coli, we developed a comprehensive screening strategy guided by the principle of maximum drug-likeness. An integrated evaluation framework was established, consisting of 33 independent predictive submodels across five dimensions: physicochemical properties, pharmacokinetics, safety, efficacy, and stability. This framework was combined with a five-fold property-spectrum scoring mechanism (S5F) to enable multidimensional and quantitative prioritisation of candidates based on overall developability. Application of this strategy to the eMolecules library yielded 15 high-potential candidates. Experimental results showed compound M3 as the lead molecule, exhibiting notable antibacterial activity against E. coli (minimum inhibitory concentration [MIC] = 16 μg/mL). Molecular analyses further demonstrated that M3 achieved superior binding stability relative to the reference drug Cefaclor through a multimodal, high-affinity interaction network with the target protein. This strategy reduces late-stage attrition risk and provides a robust paradigm for rational antibacterial drug discovery.