Patankar S J, Jurs P C
Department of Chemistry, 152 Davey Laboratory, Penn State University, University Park, Pennsylvania 16802, USA.
J Chem Inf Comput Sci. 2003 May-Jun;43(3):885-99. doi: 10.1021/ci020045e.
Loss of Protein Tyrosine Phosphatase 1B (PTP 1B) activity is known to enhance insulin sensitivity and resistance to weight gain. So potent and orally active PTP1B inhibitors could be potential pharmacological agents for the treatment of Type 2 diabetes and obesity. Classification models of PTP1B inhibitors are developed using a data set containing 128 compounds. Their inhibitory concentrations ranged from -1.59 to 1.68 log units. Initially a two-class (active, inactive) problem is tackled using a number of different methods. The data set was divided into active and inactive classes on the basis of inhibitory activity of the compounds. Molecular structure-based descriptors were calculated and used in the model development. Descriptors encoding the flexibility of the molecules were investigated. Classification models were generated using k-nearest neighbors (k-NN), linear discriminant analysis (LDA), and radial basis function neural network (RBFNN). All models are tested using an external prediction set, compounds not used anywhere during the model development procedure. A five-descriptor model is developed that produces a classification rate of 85.7% for an external prediction set. Then a three-class (active, moderately active, inactive) problem was explored. This time the data set was divided into highly active, moderate, and inactive classes on the basis of inhibitory activity of the compounds. The best classification rate achieved for an external prediction set was 85%. The classification rates achieved indicate that these models could serve as a screening mechanism, to identify potentially useful PTP 1B inhibitors. In addition multiple linear regression and computational neural network models are also developed for prediction of log IC(50) values. All QSAR models are tested using the same external prediction set.
已知蛋白酪氨酸磷酸酶1B（PTP 1B）活性丧失可增强胰岛素敏感性并抵抗体重增加。因此，强效且口服活性的PTP1B抑制剂可能是治疗2型糖尿病和肥胖症的潜在药物。使用包含128种化合物的数据集开发了PTP1B抑制剂的分类模型。它们的抑制浓度范围为-1.59至1.68对数单位。最初，使用多种不同方法解决两类（活性、非活性）问题。根据化合物的抑制活性将数据集分为活性和非活性类别。计算基于分子结构的描述符并将其用于模型开发。研究了编码分子灵活性的描述符。使用k近邻（k-NN）、线性判别分析（LDA）和径向基函数神经网络（RBFNN）生成分类模型。所有模型均使用外部预测集进行测试，即在模型开发过程中未在任何地方使用过的化合物。开发了一个五描述符模型，该模型对外部预测集的分类率为85.7%。然后探索了三类（活性、中度活性、非活性）问题。这次根据化合物的抑制活性将数据集分为高活性、中度活性和非活性类别。外部预测集实现的最佳分类率为85%。所实现的分类率表明这些模型可作为一种筛选机制，以识别潜在有用的PTP 1B抑制剂。此外，还开发了多元线性回归和计算神经网络模型来预测log IC(50)值。所有QSAR模型均使用相同的外部预测集进行测试。
