Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
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DOI：
10.1186/s13321-015-0069-3
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发表时间：
2015
影响因子：
8.6
通讯作者：
Héberger K
中科院分区：
文献类型：
--
作者：
Bajusz D;Rácz A;Héberger K
化学信息学家在进行分子相似性计算时配备了非常丰富的工具箱。存在大量的分子表示，并且存在几种方法（相似性和距离度量）来量化分子表示的相似性。在这项工作中，八个著名的相似性/距离度量进行了比较，在一个大的数据集的分子指纹与排名差异（SRD）和方差分析的总和。分子大小，选择方法和数据预处理方法对比较结果的影响也进行了评估。供应商数据库（https：//mcule.com/）用作本研究相似性计算的化合物来源。汇编了大量数据集，每个数据集由100种化合物组成，生成分子指纹图谱，并计算每个数据集随机选择的参比化合物与其余化合物之间的相似性值。相似性度量基于化合物在一个实验（一个数据集）内的排序使用排序差异之和（SRD）进行比较，而整个实验组的结果总结在箱线图上。最后，用方差分析（ANOVA）评价各种因素（数据预处理、分子大小、选择方法）的影响。这项研究补充了以前的努力，检查和排名的分子相似性计算的各种指标。然而，这里采取了一种完全笼统的方法，忽略了对所涉化合物的任何先验知识，以及仅审查一种或几种具体情况所带来的任何偏见。Tanimoto指数、Dice指数、余弦系数和Soergel距离被确定为用于相似性计算的最佳（并且在某种意义上等效）度量，即这些度量可以产生最接近于八个度量的复合（平均）排名的排名。不推荐从欧几里德和曼哈顿距离导出的相似性度量本身，尽管它们与其他相似性度量的可变性和多样性在某些情况下（例如，对于数据融合）可能是有利的。还得出结论的分子大小，选择方法和数据预处理的排名行为的研究指标的影响。相似性度量与排序差异之和（SRD）比较的可视化摘要。本文的在线版本（doi：10.1186/s13321-015-0069-3）包含补充材料，可供授权用户使用。
Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. A supplier database (https://mcule.com/) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. A visual summary of the comparison of similarity metrics with sum of ranking differences (SRD). The online version of this article (doi:10.1186/s13321-015-0069-3) contains supplementary material, which is available to authorized users.
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DOI：
10.1021/ci970437z
发表时间：
1998-05-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Flower, DR
通讯作者：
Flower, DR
影响因子：
--
作者：
Reisen, Felix;Zhang, Xian;Selzer, Paul
通讯作者：
Selzer, Paul
影响因子：
5.6
作者：
Bender, Andreas;Jenkins, Jeremy L.;Davies, John W.
通讯作者：
Davies, John W.
DOI：
10.1021/ci990316u
发表时间：
2000-01-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Godden, JW;Xue, L;Bajorath, J
通讯作者：
Bajorath, J
影响因子：
7.3
作者：
Maggiora, Gerald;Vogt, Martin;Bajorath, Juergen
通讯作者：
Bajorath, Juergen