Singh Monika, Dureja Harish, Madan A K
Faculty of Pharmaceutical Sciences, M.D. University, Rohtak 124-001, India.
Faculty of Pharmaceutical Sciences, Pt. B.D. Sharma University of Health Sciences, Rohtak 124-001, India.
Int J Comput Biol Drug Des. 2014;7(4):319-40. doi: 10.1504/IJCBDD.2014.066539. Epub 2014 Dec 25.
In present study, adjacent path eccentric distance sum indices proposed in Part-I of the manuscript were successfully utilised for the development of models for cycloxygenase-2 (COX-2) inhibitory activity. Values of diverse molecular descriptors (MDs) for each of 38 indomethacin analogues involved in the dataset were computed. A total of 55 diverse MDs were ultimately shortlisted for further analysis. The suitable models were developed using decision tree (DT), random forest (RF) and moving average analysis (MAA). The DT identified the proposed topological index (TI)-(A)ξ(3)(PDS) as one of the important indices. The accuracy of prediction of DT, RF and MAA-based models varied from 81.58% to 97.37%. The statistical significance of proposed models was assessed through inter-correlation analysis, sensitivity, specificity, non-error rate and Mathews correlation coefficient. Proposed models offer vast potential for providing lead structures for the development of potent anti-inflammatory agents devoid of COX-1 side effects.
在本研究中,手稿第一部分提出的相邻路径偏心距总和指数成功用于开发环氧化酶-2(COX-2)抑制活性模型。计算了数据集中38种吲哚美辛类似物各自的多种分子描述符(MDs)值。最终筛选出55种不同的MDs用于进一步分析。使用决策树(DT)、随机森林(RF)和移动平均分析(MAA)开发了合适的模型。DT将所提出的拓扑指数(TI)-(A)ξ(3)(PDS)确定为重要指数之一。基于DT、RF和MAA的模型的预测准确率在81.58%至97.37%之间。通过相互关联分析、敏感性、特异性、无错误率和马修斯相关系数评估了所提出模型的统计学意义。所提出的模型为开发无COX-1副作用的强效抗炎药提供先导结构具有巨大潜力。