Begum S, Achary P Ganga Raju
a Department of Chemistry , Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan University , Bhubaneswar , Odisha - 751030 , India.
SAR QSAR Environ Res. 2015;26(5):343-61. doi: 10.1080/1062936X.2015.1039577. Epub 2015 May 13.
Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.
构建了定量构效关系（QSAR）模型，用于预测43种强效抑制剂对丝裂原活化蛋白激酶相互作用蛋白激酶（MNK1）的抑制作用（pIC50，即50%有效浓度的负对数）。采用简化分子线性输入系统（SMILES）表示分子结构，对pIC50值进行了五次随机拆分建模。通过三种方法的蒙特卡罗优化进行QSAR模型构建：经典方案；相关性平衡；以及与理想斜率的平衡相关性。通过rm(2)、r(*)m(2)、[公式：见正文]和随机化技术等参数检查这些模型的稳健性。基于单一最优描述符的最佳QSAR模型被用于研究6-(4-(2-(哌啶-1-基)乙氧基)苯基)-3-(吡啶-4-基)吡唑并[1,5-a]嘧啶衍生物的体外构效关系，作为开发新型强效MNK1抑制剂的筛选工具。还报道了烷基、-OH、-NO2、F、Cl、Br、I等对抑制MNK1的IC50值的影响。
