Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China.
School of Materials Science and Engineering, Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450001, China.
Int J Mol Sci. 2021 Sep 6;22(17):9645. doi: 10.3390/ijms22179645.
The ionotropic GABA receptor (GABAR) has been proven to be an important target of atypical antipsychotics. A novel series of imidazo [1,2-a]-pyridine derivatives, as selective positive allosteric modulators (PAMs) of α1-containing GABARs with potent antipsychotic activities, have been reported recently. To better clarify the pharmacological essentiality of these PAMs and explore novel antipsychotics hits, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) were performed on 33 imidazo [1,2-a]-pyridines. The constructed 3D-QSAR models exhibited good predictive abilities. The dockings results and MD simulations demonstrated that hydrogen bonds, π-π stackings, and hydrophobic interactions play essential roles in the binding of these novel PAMs in the GABAR binding pocket. Four hit compounds () were then screened out by the combination of the constructed models and computations, including the pharmacophore model, Topomer Search, molecular dockings, ADME/T predictions, and MD simulations. The compounds and with higher docking scores and better predicted activities, were also found to be relatively stable in the binding pocket by MD simulations. These results might provide a significant theoretical direction or information for the rational design and development of novel α1-GABAR PAMs with antipsychotic activities.
离子型 GABA 受体 (GABAR) 已被证明是新型抗精神病药物的重要靶标。最近报道了一系列新型咪唑并[1,2-a]吡啶衍生物,它们作为具有潜在抗精神病活性的 α1 含 GABA 受体的选择性正变构调节剂 (PAM)。为了更好地阐明这些 PAMs 的药理学重要性并探索新型抗精神病药物,对 33 种咪唑并[1,2-a]吡啶进行了三维定量构效关系 (3D-QSAR)、分子对接、药效团建模和分子动力学 (MD)研究。构建的 3D-QSAR 模型具有良好的预测能力。对接结果和 MD 模拟表明,氢键、π-π 堆积和疏水相互作用在这些新型 PAMs 与 GABA 受体结合口袋的结合中起着重要作用。然后通过构建模型和计算的结合,筛选出四个命中化合物(),包括药效团模型、Topomer Search、分子对接、ADME/T 预测和 MD 模拟。通过 MD 模拟发现,具有更高对接分数和更好预测活性的化合物和在结合口袋中也相对稳定。这些结果可能为新型具有抗精神病活性的 α1-GABAR PAMs 的合理设计和开发提供重要的理论方向或信息。