Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, P.O. Box 11562, Cairo, Egypt.
Molecular Modeling Unit, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, P.O. Box 11562, Cairo, Egypt.
J Mol Model. 2019 May 25;25(6):171. doi: 10.1007/s00894-019-4032-5.
Considering the emerging importance of glycogen synthase kinase 3 beta (GSK-3β) inhibitors in treatment of Alzheimer's disease, multi-protein structure receptor-based pharmacophore modeling was adopted to generate a 3D pharmacophore model for (GSK-3β) inhibitors. The generated 3D pharmacophore was then validated using a test set of 1235 compounds. The ZINCPharmer web tool was used to virtually screen the public ZINC database using the generated 3D pharmacophore. A set of 12,251 hits was produced and then filtered according to their lead-like properties, predicted central nervous system (CNS) activity, and Pan-assay interference compounds (PAINS) fragments to 630 compounds. Scaffold Hunter was then used to cluster the filtered compounds according to their chemical structure framework. From the different clusters, 123 compounds were selected to cover the whole chemical space of the obtained hits. The SwissADME online tool was then used to filter out the compounds with undesirable pharmacokinetic properties giving a set of 91 compounds with promising predicted pharmacodynamic and pharmacokinetic properties. To confirm their binding capability to the GSK-3β binding site, molecular docking simulations were performed for the final 91 compounds in the GSK-3β binding site. Twenty-five compounds showed acceptable binding poses that bind to the key amino acids in the binding site Asp133 and Val135 with good binding scores. The quinolin-2-one derivative ZINC67773573 was found to be a promising lead for designing new GSK-3β inhibitors for Alzheimer's disease treatment. Graphical abstract A 3D pharmacophore model for the discovery of novel (GSK-3β) inhibitors.
考虑到糖原合酶激酶 3β(GSK-3β)抑制剂在治疗阿尔茨海默病中的重要性不断增加,采用多蛋白结构受体的基于药效团的药物设计方法,生成了用于(GSK-3β)抑制剂的三维药效团模型。然后使用 1235 个化合物的测试集对生成的 3D 药效团进行验证。使用生成的 3D 药效团,使用 ZINCPharmer 网络工具对公共 ZINC 数据库进行虚拟筛选。产生了一组 12,251 个命中物,然后根据它们的类药性、预测的中枢神经系统(CNS)活性和 Pan-assay 干扰化合物(PAINS)片段对其进行过滤,得到 630 个化合物。然后使用 Scaffold Hunter 根据其化学结构框架对过滤后的化合物进行聚类。从不同的簇中,选择 123 个化合物来覆盖获得的命中物的整个化学空间。然后使用 SwissADME 在线工具过滤掉具有不理想的药代动力学性质的化合物,得到一组具有良好预测药效动力学性质的 91 个化合物。为了确认它们与 GSK-3β 结合位点的结合能力,对最终的 91 个化合物在 GSK-3β 结合位点进行了分子对接模拟。25 个化合物显示出可接受的结合构象,它们与结合位点中的关键氨基酸 Asp133 和 Val135 结合,并具有良好的结合评分。发现喹啉-2-酮衍生物 ZINC67773573 是设计用于治疗阿尔茨海默病的新型 GSK-3β 抑制剂的有前途的先导化合物。