Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier
Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier
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DOI：
10.1021/ci034207y
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发表时间：
2004-01-01
期刊：
影响因子：
--
通讯作者：
Reiling, S
中科院分区：
文献类型：
--
作者：
Bender, A;Mussa, HY;Reiling, S
介绍了一种相似性搜索的新技术。分子由原子环境表示，原子环境被输入到基于信息增益的特征选择中。然后使用朴素贝叶斯分类器进行复合分类。新方法通过其检索 MDL 药物数据报告 (MDDR) 中植入的五组活性分子的能力进行了测试。在比较实验中，该算法优于此处使用二维和三维描述符评估的所有当前检索方法，并深入了解结构成分对结合的重要性。
A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naive Bayesian classifier is then employed for compound classification. The new method is tested by its ability to retrieve five sets of active molecules seeded in the MDL Drug Data Report (MDDR). In comparison experiments, the algorithm outperforms all current retrieval methods assessed here using two- and three-dimensional descriptors and offers insight into the significance of structural components for binding.