基于绕行矩阵的(Q)SAR/QSPR相邻路径偏心距离和指标。第二部分:吲哚美辛衍生物COX-2抑制活性模型的建立。

Q4 Pharmacology, Toxicology and Pharmaceutics
Monika Singh, Harish Dureja, A K Madan
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引用次数: 0

摘要

在本研究中,本文第一部分中提出的相邻路径偏心距离和指数成功地用于开发环氧化酶-2 (COX-2)抑制活性的模型。计算了数据集中涉及的38种吲哚美辛类似物的不同分子描述符(MDs)的值。最终共有55个不同的MDs被列入进一步分析的候选名单。采用决策树(DT)、随机森林(RF)和移动平均分析(MAA)建立了合适的模型。DT将提出的拓扑指数(TI)-(A)ξ(3)(PDS)作为重要指标之一。DT、RF和maa模型的预测准确率在81.58% ~ 97.37%之间。通过相关分析、敏感性、特异性、非错误率和Mathews相关系数评价模型的统计学意义。所提出的模型为开发无COX-1副作用的强效抗炎药提供了巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detour matrix-based adjacent path eccentric distance sum indices for (Q)SAR/QSPR. Part II: application in development of models for COX-2 inhibitory activity of indomethacin derivatives.

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.

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来源期刊
International Journal of Computational Biology and Drug Design
International Journal of Computational Biology and Drug Design Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
1.00
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