Centre of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran.
J Enzyme Inhib Med Chem. 2010 Dec;25(6):844-53. doi: 10.3109/14756361003757893. Epub 2010 Apr 30.
A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.
提出了一个线性定量构效关系（QSAR）模型，用于对酰胺和咪唑并[1,2-α]吡啶的白细胞介素-1 受体相关激酶 4（IRAK-4）抑制活性进行建模和预测。该模型是使用多元线性回归（MLR）技术在一个由 65 种最近发现的酰胺和咪唑并[1,2-α]吡啶组成的数据库上生成的。在所考虑的作为模型输入的不同结构、拓扑、几何、静电和量子化学描述符中，使用遗传算法子集选择方法（GA）选择了七个变量。通过交叉验证、通过外部测试集验证和 Y 随机化等评估技术说明了所提出的 MLR 模型的准确性。该模型的预测能力令人满意，可用于设计类似的化合物组。
