Three-dimensional QSAR using the k-nearest neighbor method and its interpretation
Three-dimensional QSAR using the k-nearest neighbor method and its interpretation
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DOI：
10.1021/ci0501286
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发表时间：
2006-01-01
影响因子：
5.6
通讯作者：
Kulkarni, SA
中科院分区：
文献类型：
--
作者：
Ajmani, S;Jadhav, K;Kulkarni, SA
在本文中，我们报告了一种新颖的三维 QSAR 方法 kNN-MFA，该方法基于 k 最近邻方法的原理并结合各种变量选择程序而开发。 kNN-MFA 方法用于生成三个不同数据集的模型，并通过每个模型预测测试分子的活性。使用的三个数据集是标准类固醇基准、抗炎数据集和抗癌数据集。该研究的结果是，对于所有三个数据集，kNN-MFA 模型都比报告的 CoMFA 模型具有更好的统计参数。研究还发现，与逐步向前选择过程相比，随机方法可以生成更好的模型，从而产生更准确的预测。因此，kNN-MFA 方法是类 CoMFA 方法的良好替代方法。
In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA approach was used to generate models for three different data sets and predict the activity of test molecules through each of these models. The three data sets used were the standard steroid benchmark, an antiinflammatory and an anticancerous data set. The study resulted in kNN-MFA models having better statistical parameters than the reported CoMFA models for all the three data sets. It was also found that stochastic methods generate better models resulting in more accurate predictions as compared to stepwise forward selection procedures. Thus, kNN-MFA method represents a good alternative to CoMFA-like methods.