Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds
Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds
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DOI：
10.1080/1062936032000169642
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发表时间：
2004-02-01
影响因子：
3
通讯作者：
Tuppurainen, KA
中科院分区：
文献类型：
--
作者：
Asikainen, AH;Ruuskanen, J;Tuppurainen, KA
最近引入了一种基于 k 最近邻 (kNN) 原理的新方法（在定量构效关系 (QSAR) 的背景下），用于推导预测构效关系。其性能已经过测试，用于估计 142 种不同有机分子的雌激素结合亲和力。已经获得了高度预测的模型。此外，已经证明，从各个 QSAR 模型的算术平均值导出的共识型 kNN QSAR 模型在统计上具有鲁棒性，并且比大多数单独的 QSAR 模型提供了更准确的预测。最后，使用来自广泛使用的类固醇基准数据集的 3D QSAR 和 log P 数据测试了共识 QSAR 方法。
A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.