Department of Chemistry, S K Government College, Sikar, Rajasthan, India.
J Enzyme Inhib Med Chem. 2011 Jun;26(3):412-21. doi: 10.3109/14756366.2010.519702. Epub 2010 Oct 13.
The histamine H(4) receptor binding affinity of 2-(4-methylpiperazin-1-yl)quinoxaline derivatives has been quantitatively analyzed in terms of Dragon descriptors. The derived QSAR models have provided rationales to explain the activity of titled derivatives. The descriptors identified in CP-MLR analysis have highlighted the role of path/walk 4-Randic shape index (PW4), mean square distance (MSD) index, topological charges (GGI9, JGI2, and JGI7), atomic properties in respective lags of 2D-autocorrelations (MATS7e, GATS7e, and MATS8p), and Burden matrix (BELm1) to explain the binding affinity. Certain structural fragments (C-002 and C-027) have also shown prevalence to optimize the H(4)R binding affinity of titled compounds. The PLS analysis has also confirmed the dominance of information content of CP-MLR-identified descriptors for modelling the activity.
已用量子类似关系(QSAR)方法中的 Dragon 描述符对 2-(4-甲基哌嗪-1-基)喹喔啉衍生物的组胺 H(4)受体结合亲和力进行了定量分析。所得到的 QSAR 模型为解释标题衍生物的活性提供了依据。CP-MLR 分析中确定的描述符突出了路径/步长 4-Randic 形状指数 (PW4)、均方距离 (MSD) 指数、拓扑电荷 (GGI9、JGI2 和 JGI7)、二维自相关各滞后的原子性质 (MATS7e、GATS7e 和 MATS8p) 和负担矩阵 (BELm1) 的作用,以解释结合亲和力。某些结构片段 (C-002 和 C-027) 也显示出对优化标题化合物的 H(4)R 结合亲和力的优势。PLS 分析也证实了 CP-MLR 鉴定的描述符对模型活性的信息含量的主导地位。