Ugale V G, Bari S B
a Department of Pharmaceutical Chemistry , R. C. Patel Institute of Pharmaceutical Education and Research , Shirpur ( Dhule ), Maharashtra , India.
b Department of Pharmaceutical Chemistry , H. R. Patel Institute of Pharmaceutical Education and Research , Shirpur ( Dhule ), Maharashtra , India.
SAR QSAR Environ Res. 2016;27(2):125-45. doi: 10.1080/1062936X.2015.1136679.
The Gly/NMDA receptor has become known as potential target for the management of neurodegenerative diseases. Discovery of Gly/NMDA antagonists has thus attracted much attention in recent years. In the present research, a cheminformatics approach has been used to determine structural requirements for Gly/NMDA antagonism and to identify potential antagonists. Here, 37 quinoxaline derivatives were selected to develop a significant pharmacophore model with good certainty. The selected model was validated by leave-one-out cross-validation, an external test set, decoy set and Y-randomization test. Applicability domain was verified by the standardization approach. The validated 3D-QSAR model was used to screen virtual hits from the ZINC database by pharmacophore mapping. Molecular docking was used for assessment of receptor-ligand binding modes and binding affinities. The GlideScore and molecular interactions with critical amino acids were considered as crucial features to identify final hits. Furthermore, hits were analysed for in silico pharmacokinetic parameters and Lipinski's rule of five, demonstrating their potential as drug-like candidates. The PubChem and SciFinder search tools were used to authenticate the novelty of leads retrieved. Finally, five different leads have been suggested as putative novel candidates for the exploration of potent Gly/NMDA receptor antagonists.
甘氨酸/N-甲基-D-天冬氨酸(Gly/NMDA)受体已成为神经退行性疾病治疗的潜在靶点。因此,近年来甘氨酸/NMDA拮抗剂的发现备受关注。在本研究中,采用了一种化学信息学方法来确定甘氨酸/NMDA拮抗作用的结构要求,并识别潜在的拮抗剂。在此,选择了37种喹喔啉衍生物来构建一个具有较高可信度的显著药效团模型。通过留一法交叉验证、外部测试集、诱饵集和Y随机化测试对所选模型进行了验证。通过标准化方法验证了适用域。经验证的3D-QSAR模型用于通过药效团映射从ZINC数据库中筛选虚拟命中物。分子对接用于评估受体-配体结合模式和结合亲和力。GlideScore和与关键氨基酸的分子相互作用被视为识别最终命中物的关键特征。此外,对命中物进行了计算机模拟药代动力学参数和Lipinski五规则分析,证明了它们作为类药物候选物的潜力。使用PubChem和SciFinder搜索工具来验证所检索先导物的新颖性。最后,提出了五种不同的先导物作为探索强效甘氨酸/NMDA受体拮抗剂的假定新型候选物。