Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms
Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms
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DOI：
10.1089/10665270260518317
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发表时间：
2002-01-01
影响因子：
1.7
通讯作者：
Yeh, J
中科院分区：
文献类型：
--
作者：
Doniger, S;Hofmann, T;Yeh, J
两种不同的机器学习算法已被用于预测不同类别分子的血脑屏障渗透性，以开发预测药物化合物穿透CNS的能力的方法。第一种算法是基于多层感知器神经网络和第二种算法使用支持向量机。两种算法都是在由179个CNS活性分子和145个CNS非活性分子组成的相同数据集上训练的。训练参数包括分子量、亲脂性、氢键和决定分子扩散穿过膜的能力的其他变量。结果表明，支持向量机优于神经网络。基于超过30个不同的验证集，SVM可以正确预测高达96%的分子，平均81.5%超过30个测试集，其中包括相同数量的CNS阳性和阴性分子。这是相当有利的相比，神经网络的平均性能为75.7%，具有相同的30个测试集。SVM算法的结果非常令人鼓舞，并表明像这样的分类工具将被证明是一种有价值的预测方法。
Two different machine-learning algorithms have been used to predict the blood-brain barrier permeability of different classes of molecules, to develop a method to predict the ability of drug compounds to penetrate the CNS. The first algorithm is based on a multilayer perceptron neural network and the second algorithm uses a support vector machine. Both algorithms are trained on an identical data set consisting of 179 CNS active molecules and 145 CNS inactive molecules. The training parameters include molecular weight, lipophilicity, hydrogen bonding, and other variables that govern the ability of a molecule to diffuse through a membrane. The results show that the support vector machine outperforms the neural network. Based on over 30 different validation sets, the SVM can predict up to 96% of the molecules correctly, averaging 81.5% over 30 test sets, which comprised of equal numbers of CNS positive and negative molecules. This is quite favorable when compared with the neural network's average performance of 75.7% with the same 30 test sets. The results of the SVM algorithm are very encouraging and suggest that a classification tool like this one will prove to be a valuable prediction approach.