Differential shannon entropy as a sensitive measure of differences in database variability of molecular descriptors
Differential shannon entropy as a sensitive measure of differences in database variability of molecular descriptors
复制标题
DOI：
10.1021/ci0102867
复制
发表时间：
2001-07-01
期刊：
影响因子：
--
通讯作者：
Bajorath, J
中科院分区：
文献类型：
--
作者：
Godden, JW;Bajorath, J
介绍了一种比较化合物数据库间分子描述符信息量和方差差异的方法--微分香农熵（DSE）。该分析是基于直方图记录的个人和分组分布的分子描述符和计算香农熵（SE），最初应用于数字通信的形式主义。我们最近表明，SE值反映了描述符设置的非参数变化。现在，该分析已被推进到评估包含合成化合物、天然产物或药物样分子的数据库中143个分子描述符的信息内容差异。DSE度量捕获描述符分布补充或重复分子数据库中包含的信息的程度。在我们的分析中，我们观察到一些描述符的显着差异，并根据其相关的DSE值对它们进行排名。使用DSE计算，不同类型的描述符的相对信息内容可以被量化，即使差异是微妙的。
A method termed Differential Shannon Entropy (DSE) is introduced to compare differences in information content and variance of molecular descriptors between compound databases. The analysis is based on histograms recording the individual and grouped distributions of molecular descriptors and calculation of Shannon entropy (SE), a formalism originally applied to digital communication. We have recently shown that SE values reflect the nonparametric variability of descriptor settings. Now the analysis has been advanced to assess differences in information content of 143 molecular descriptors in databases containing synthetic compounds, natural products, or drug-like molecules. The DSE metric captures the degree to which descriptor distributions complement or duplicate information contained in molecular databases. In our analysis, we observe significant differences for a number of descriptors and rank them according to their associated DSE values. Using DSE calculations, relative information content of different types of descriptors can be quantified, even if differences are subtle.