Division of Medicinal and Process Chemistry, CSIR Central Drug Research Institute, Lucknow, India.
SAR QSAR Environ Res. 2012 Jul;23(5-6):389-407. doi: 10.1080/1062936X.2012.664824. Epub 2012 Mar 27.
The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.
采用药效团模型构建、对接和生成的聚焦虚拟库的虚拟筛选（HVS）研究，以鉴定新型高亲和力和选择性β（3）-肾上腺素能受体（β-AR）激动剂。该研究基于使用Discovery Studio 中实施的药效团生成方案对 51 个β（3）-AR 激动剂的训练集，开发了一个包含一个氢键供体、两个疏水性、一个正可离子化和一个负可离子化特征的最佳药效团模型。该模型进一步通过测试集、外部集以及药效团特征补充使用 MODELLER 软件开发的同源建模β（3）-AR 活性位点氨基酸的能力进行了验证。基于我们之前报道的一项全面研究的结构见解生成了聚焦虚拟库，该研究集中于代表激动剂和拮抗剂的β-AR 亚型选择性的结构基础。通过顺序使用最佳药效团模型和同源建模β（3）-AR 对生成的聚焦库进行 HVS，已经确定了潜在的虚拟先导化合物，作为新型高亲和力和选择性β（3）-AR 激动剂。
