How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection
How Do 2D Fingerprints Detect Structurally Diverse Active Compounds? Revealing Compound Subset-Specific Fingerprint Features through Systematic Selection
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DOI：
10.1021/ci200275m
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发表时间：
2011-09-01
影响因子：
5.6
通讯作者：
Bajorath, Juergen
中科院分区：
文献类型：
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作者：
Heikamp, Kathrin;Bajorath, Juergen
在独立的研究中，先前已经证明二维（2D）指纹在虚拟筛选中具有支架跳跃能力，尽管这些描述符主要强调参考化合物和数据库化合物的结构和/或拓扑相似性。然而，这种指纹富集数据库选择集中结构多样的分子的机制目前还不太清楚。为了解决这个问题，120个化合物的活性类不同的结构多样性进行了相似性搜索计算使用原子环境指纹。两个特征选择方法，Kullback-Leibler分歧和增益比分析，应用到系统地减少这些指纹，并产生可供选择的版本进行搜索。增益比是信息论中的一种特征选择方法，迄今为止尚未在指纹分析中被考虑。然而，它被证明是一种有效的指纹特征选择方法。通过比较特征选择和相似性搜索，详细分析了原始指纹和缩减指纹的复合召回特性。发现指纹特征的小集合可以将活性化合物的子集与其他数据库分子区分开。指纹相似性搜索的化合物召回通常是由不同指纹特征对不同化合物子集的累积检测引起的，这为这些2D指纹的支架跳跃潜力提供了理论基础。
In independent studies it has previously been demonstrated that two-dimensional (2D) fingerprints have scaffold hopping ability in virtual screening, although these descriptors primarily emphasize structural and/or topological resemblance of reference and database compounds. However, the mechanism by which such fingerprints enrich structurally diverse molecules in database selection sets is currently little understood. In order to address this question, similarity search calculations on 120 compound activity classes of varying structural diversity were carried out using atom environment fingerprints. Two feature selection methods, Kullback-Leibler divergence and gain ratio analysis, were applied to systematically reduce these fingerprints and generate alternative versions for searching. Gain ratio is a feature selection method from information theory that has thus far not been considered in fingerprint analysis. However, it is shown here to be an effective fingerprint feature selection approach. Following comparative feature selection and similarity searching, the compound recall characteristics of original and reduced fingerprint versions were analyzed in detail. Small sets of fingerprint features were found to distinguish subsets of active compounds from other database molecules. The compound recall of fingerprint similarity searching often resulted from a cumulative detection of distinct compound subsets by different fingerprint features, which provided a rationale for the scaffold hopping potential of these 2D fingerprints.