SIMILARITY SEARCHING AND CLUSTERING OF CHEMICAL-STRUCTURE DATABASES USING MOLECULAR PROPERTY DATA
SIMILARITY SEARCHING AND CLUSTERING OF CHEMICAL-STRUCTURE DATABASES USING MOLECULAR PROPERTY DATA
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DOI：
10.1021/ci00021a011
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发表时间：
1994-09-01
期刊：
影响因子：
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通讯作者：
FISANICK, W
中科院分区：
文献类型：
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作者：
DOWNS, GM;WILLETT, P;FISANICK, W
以前关于化学结构数据库聚类的工作主要集中在基于各种结构特征的分子间相似性度量的使用上。在本文中，我们报告了5982个分子的最近邻搜索和聚类实验，每个分子具有13个计算的全局分子性质。最近邻算法是一个上界过程，它使用三角形不等式来最小化在度量空间中搜索最近邻时需要执行的距离计算次数。我们的实验表明，当需要少量的最近邻时，它表现得很好，但是当需要大量的近邻时，例如要进行聚类时，基本的“蛮力”过程是最好的。测试的聚类方法有Ward和群平均分层聚类法、最小直径合成分层分裂法和Jarvis-Patrick最近邻法。我们的实验表明，前三种方法得到了相似的结果，是用性质数据表征分子聚类的最佳方法。Jarvis-Patrick方法被广泛用于以结构片段为特征的分子聚类，但其效果不如其他方法。
Previous work on the clustering of chemical-structure databases has focused on the use of intermolecular similarity measures that are based on structural features of various kinds. In this paper, we report nearest-neighbor searching and clustering experiments with a set of 5982 molecules, each of which is characterized by 13 calculated global molecular properties. The nearest-neighbor algorithm is an upperbound procedure that uses the triangle inequality to minimize the number of distance calculations that need to be carried out when searching for nearest neighbors in metric spaces. Our experiments suggest that it performs well when small numbers of nearest neighbors are required, but that the basic ''brute-force'' procedure is best when large numbers are needed, such as when clustering is to be carried out. The clustering methods tested are the Ward and group-average hierarchic agglomerative methods, the minimum-diameter polythetic hierarchic divisive method, and the Jarvis-Patrick nearest-neighbor method. Our experiments suggest that the first three methods, which gave similar results, are the best methods for clustering molecules characterized by property data. The Jarvis-Patrick method, which has been extensively used for clustering molecules characterized by structural fragments, was not as effective as these other methods.