Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad, India.
SAR QSAR Environ Res. 2013 Aug;24(8):625-45. doi: 10.1080/1062936X.2013.792871. Epub 2013 May 28.
This study has investigated docking-based 3D quantitative structure-activity relationships (QSARs) for a range of quinoline carboxylic acid derivatives by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A docking study has shown that most of the compounds formed H-bonds with Arg136 and Gln47, which have already been shown to be essential for the binding of ligands at the active site of the hydroorotate dehydrogenase adenovirus (hDHODH). Bioactive conformations of all the molecules obtained from the docking study were used for the 3D QSAR study. The best CoMFA and CoMSIA models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q²) of 0.672 and 0.613, r² cv of 0.635 and 0.598 and coefficients of determination (r²) of 0.963 and 0.896, respectively. Both models were validated by a test set of 15 compounds, giving satisfactory predicted correlation coefficients (r² pred) of 0.824 and 0.793 for the CoMFA and CoMSIA models, respectively. From the docking-based 3D QSAR study we designed 34 novel quinoline-based compounds and performed structure-based virtual screening. Finally, in silico pharmacokinetics and toxicities were predicted for 24 of the best docked molecules. The study provides valuable information for the understanding of interactions between hDHODH and the novel compounds.
本研究通过比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对一系列喹啉羧酸衍生物进行了基于对接的 3D 定量构效关系(QSAR)研究。对接研究表明,大多数化合物与 Arg136 和 Gln47 形成氢键,这两者已被证明对配体在 hydroorotate 脱氢酶腺病毒(hDHODH)活性部位的结合至关重要。从对接研究中获得的所有分子的生物活性构象都用于 3D QSAR 研究。为训练集获得了最佳的 CoMFA 和 CoMSIA 模型,并且发现这些模型具有统计学意义,交叉验证系数(q²)分别为 0.672 和 0.613,验证相关系数(r² cv)分别为 0.635 和 0.598,决定系数(r²)分别为 0.963 和 0.896。这两个模型均通过 15 个化合物的测试集进行了验证,CoMFA 和 CoMSIA 模型的预测相关系数(r² pred)分别为 0.824 和 0.793,结果令人满意。通过基于对接的 3D QSAR 研究,我们设计了 34 种新型喹啉类化合物,并进行了基于结构的虚拟筛选。最后,对 24 种最佳对接分子进行了体内药代动力学和毒性预测。该研究为理解 hDHODH 与新型化合物之间的相互作用提供了有价值的信息。