ABSTRACT As a critical step in drug discovery, lead optimization is a profoundly complex endeavor with a notoriously high failure rate, as it necessitates the simultaneous optimization of multiple, often conflicting parameters, including physicochemical properties, drug‐likeness, synthetic accessibility, and target binding affinity. While several generative models have been proposed for lead optimization under multi‐property constraints, they still struggle to balance multi‐objective optimization with sufficient scaffold‐level exploration. To address this challenge, we present SMarT‐Diff (Scaffold‐based Multi‐property Tuning Diffusion), a generative diffusion model that achieves this balance by reinventing scaffold hopping—enabling both property optimization and structural novelty. SMarT‐Diff achieved superior performance across diverse molecular generation and optimization metrics. Notably, across both single‐target (LRRK2, HPK1, GLP‐1R) and dual‐target (GSK3β/JNK3) molecular optimization tasks, the model consistently generated drug‐like molecules exhibiting enhanced structural diversity, preserved pharmacophoric features, and high synthetic accessibility. Furthermore, wet‐lab validation of our model‐generated compounds against LRRK2 identified a highly promising candidate with an IC 50 of 1.544 nM, which surpasses even the positive control LRRK2‐IN‐1. This result not only confirms the compound's exceptional potency but also demonstrates the strong real‐world potential of our model to drive the design and optimization of novel, highly effective drug candidates.
Yang et al. (Fri,) studied this question.