Evaluation of descriptors and classification schemes to predict cytochrome substrates in terms of chemical information

被引:4
作者
Block, John H. [1 ]
Henry, Douglas R. [2 ]
机构
[1] Oregon State Univ, Coll Pharm, Dept Pharmaceut Sci, Corvallis, OR 97330 USA
[2] BIOSAR Res, San Leandro, CA 94577 USA
关键词
cytochrome substrates; data mining; E-state indices; simple decision tree; K-nearest neighbor (K-NN); logistic regression; molecular properties; Naive-Bayes; ripper; structural keys; support vector machine (SVM); topological indices;
D O I
10.1007/s10822-008-9176-9
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Using a small database of defined substrates in humans for cytochrome P450 mixed function oxidases, a series of descriptors and classification methods were evaluated with respect to how well they correctly classified substrates. The descriptors ranged from structural keys to topological to electronic. A variety of classification schemes were examined in terms of their ability to point out which descriptors are important for predicting the cytochrome P450 specificity for a substrate. Results illustrate the relative effectiveness of the various kinds of descriptors and classification methods, as well as the value of using as well-defined data set as possible.
引用
收藏
页码:385 / 392
页数:8
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