Classification of Blood‐Brain Barrier Permeation by Kohonen's Self‐Organizing Neural Network (KohNN) and Support Vector Machine (SVM)
Classification of Blood‐Brain Barrier Permeation by Kohonen's Self‐Organizing Neural Network (KohNN) and Support Vector Machine (SVM)
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DOI：
10.1002/qsar.200960008
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Z. Wang;A. Yan;Qipeng Yuan
中科院分区：
文献类型：
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作者：
Z. Wang;A. Yan;Qipeng Yuan
分别采用Kohonen的自组织神经网络（KohNN）方法和支持向量机（SVM）分类方法建立血脑屏障渗透预测模型。基于五个二维属性自相关描述符，建立了多个模型，能够对 BBB 渗透和非渗透化合物进行分类，准确率超过 96%。
Kohonen's self-organizing neural network (KohNN) method and support vector machine (SVM) classification method were used to build blood-brain barrier permeation prediction model, respectively. Based on five 2D property autocorrelation descriptors, several models have been built which were able to classify BBB penetration and nonpenetration compounds with the accuracy of over 96%.