Drug discovery guided by maximum drug likeness

Abstract

To overcome the high attrition rate and limited clinical translatability in drug discovery, we introduce the concept of Maximum Drug-Likeness (MDL) and develop an applicable Fivefold MDL strategy (5F-MDL) to reshape the screening paradigm. The 5F-MDL strategy integrates an ensemble of 33 deep learning sub-models to construct a 33-dimensional property spectrum that quantifies the global phenotypic alignment of candidate molecules with clinically approved drugs along five axes: physicochemical properties, pharmacokinetics, efficacy, safety, and stability. Using drug-likeness scores derived from this 33-dimensional profile, we prioritized 15 high-potential molecules from a 16-million-molecule library. Experimental validation demonstrated that the lead compound M2 not only exhibits potent antibacterial activity, with a minimum inhibitory concentration (MIC) of 25.6 ug/mL, but also achieves binding stability superior to cefuroxime, as indicated by Molecular Mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations of -38.54 kcal/mol and a root-mean-square deviation (RMSD) of 2.8 A. This strategy could overcome scaffold constraints and offers an efficient route for discovering lead compounds with favorable prospects against drug-resistant bacteria.

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