期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 46, 期 2, 页码 569-576出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci0504418
关键词
-
This work describes the first approach in the development of a comprehensive classification method for bitterness of small molecules. The data set comprises 649 bitter and 13 530 randomly selected molecules from the MDL Drug Data Repository (MDDR) which are analyzed by circular fingerprints (MOLPRINT 2D) and information-gain feature selection. The feature selection proposes substructural features which are statistically correlated to bitterness. Classification is performed on the selected features via a naive Bayes classifier. The substructural features upon which the classification is based are able to discriminate between bitter and random compounds, and thus we propose they are also functionally responsible for causing the bitter taste. Such substructures include various sugar moieties as well as highly branched carbon scaffolds. Cynaropicrine contains a number of the substructural features found to be statistically associated with bitterness and thus was correctly predicted to be bitter by our model. Alternatively, both promethazine and saccharin contain fewer of these substructural features, and thus the bitterness in these compounds was not identified. Two different classes of-bitter compounds were identified, namely those which are larger and contain mainly oxygen and carbon and often sugar moieties, and those which are rather smaller and contain additional nitrogen and/or sulfur fragments. The classifier is able to predict 72.1% of the bitter compounds. Feature selection reduces the number of false-positives while also increasing the number of false negatives to 69.5% of bitter compounds correctly predicted. Overall, the method presented here presents both one of the largest databases of bitter compounds presently available as well as a relatively reliable classification method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据