本文采用分子模块组装策略,以靶向电压门控钾通道Q亚家族 (Potassium voltage-gated channel subfamily Q, KCNQ) 的临床候选药物派恩加滨 (HN37) 为模版进行结构优化。研究首先基于多构象分子对接与多元线性回归建立了活性预测模型;同时,通过子结构检索获取多样化的分子砌块,利用Buchwald-Hartwig反应进行模块化组装,构建了兼具高合成可及性与结构新颖性的虚拟化合物库。随后,结合类药性评价和活性预测模型对库内分子进行分级筛选,识别出一批优选结构。在此基础上,合成了24个目标化合物并采用全细胞膜片钳技术评估其激动活性。实验结果显示,共有6个化合物在100 nM浓度下可显著激动KCNQ2通道,并初步明确了构效关系。其中化合物3和4表现出良好的KCNQ2激动活性、KCNQ通道亚型选择性及代谢稳定性。本研究将基于模块组装的分子设计、分级虚拟筛选与实验验证相结合,发现了一批具有潜力的KCNQ2激动剂,为后续的结构优化奠定了基础。
In this study, a molecular module assembly strategy was employed for the structural optimization of pynegabine (HN37), a clinical candidate targeting the potassium voltage-gated channel subfamily Q (KCNQ). First, an activity prediction model was established based on multi-conformation molecular docking and multiple linear regression. Simultaneously, a virtual compound library with high synthetic accessibility and structural novelty was constructed via modular assembly based on the Buchwald-Hartwig reaction, utilizing diverse building blocks acquired through substructure searches. Subsequently, a hierarchical virtual screening workflow—combining drug-likeness evaluation and established activity prediction model—was applied to identify a set of prioritized structures. On this basis, 24 target compounds were synthesized and evaluated using whole-cell patch-clamp electrophysiology. Experimental results demonstrated that 6 compounds exhibited significant agonistic activity against the KCNQ2 channel at 100 nM, with the structure-activity relationships (SAR) preliminary elucidated. Among them, compounds 3 and 4 showed favorable KCNQ2 agonistic activity, subtype selectivity for KCNQ channels, and metabolic stability. By integrating module-assembly-based molecular design, hierarchical virtual screening, and experimental validation, this work identifies a series of promising KCNQ2 agonists and paves the way for subsequent optimization.
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