jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints.
jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints.
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DOI：
10.1186/1758-2946-3-3
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发表时间：
2011-01-10
影响因子：
8.6
通讯作者：
Zell A
中科院分区：
文献类型：
--
作者：
Hinselmann G;Rosenbaum L;Jahn A;Fechner N;Zell A
化学图形的分解是对相应有机化合物的信息进行编码的一种方便的方法。虽然有几个商业工具包可以将分子编码为所谓的指纹，但只有少数几个开源实现可用。这项工作的目的是引入一个精确定义的分子分解库，重点是这些特征在机器学习和数据挖掘中的应用。它提供了几个选项，如搜索深度、距离截止点、原子和药效团分类。此外，它还提供组合、比较或将指纹导出为几种格式的功能。我们基于开源的化学开发工具包提供了一个用于分解化学图形的Java 1.6库。我们重新实现了流行的指纹识别算法，如深度优先搜索指纹、扩展连接性指纹、自相关指纹(例如CATS2D)、径向指纹(例如Molprint 2D)、几何分子指纹、原子对和药效团指纹。我们还实现了定制指纹，例如全最短路径指纹，它只包括深度优先搜索指纹的全集路径中的最短路径子集。作为jCompoundMapper的一个应用程序，我们提供了一个命令行可执行二进制文件。我们测量了每种编码的转换速度和特征数量，并详细描述了特征的组成。在Sutherland QSAR数据集上，使用默认的参数化和支持向量机相结合来测试编码的质量。此外，我们使用大规模线性支持向量机在大规模Ames毒性基准上对指纹编码进行了基准测试。结果是有希望的，经常可以与文献结果竞争。例如，在大型Ames基准测试中，通过重新实现扩展连接指纹，我们获得了0.87的AUC ROC性能。这一结果与使用最先进的描述符的非线性支持向量机所获得的性能相当。在Sutherland QSAR数据集上，最好的指纹编码在8个基准中的5个上表现出与Sutherland等人发表的最佳描述符的结果相当或更好的性能。JCompoundMapper是一个用于化学图形指纹的库，它为开源数据挖掘工具包提供了几种调整和导出选项。数据挖掘结果的质量、转换速度、LPGL软件许可证、命令行界面和导出器对于化学信息学中的许多应用程序都应该是有用的，如对照文献方法的基准、数据挖掘算法的比较、相似性搜索和基于相似性的数据挖掘。
The decomposition of a chemical graph is a convenient approach to encode information of the corresponding organic compound. While several commercial toolkits exist to encode molecules as so-called fingerprints, only a few open source implementations are available. The aim of this work is to introduce a library for exactly defined molecular decompositions, with a strong focus on the application of these features in machine learning and data mining. It provides several options such as search depth, distance cut-offs, atom- and pharmacophore typing. Furthermore, it provides the functionality to combine, to compare, or to export the fingerprints into several formats. We provide a Java 1.6 library for the decomposition of chemical graphs based on the open source Chemistry Development Kit toolkit. We reimplemented popular fingerprinting algorithms such as depth-first search fingerprints, extended connectivity fingerprints, autocorrelation fingerprints (e.g. CATS2D), radial fingerprints (e.g. Molprint2D), geometrical Molprint, atom pairs, and pharmacophore fingerprints. We also implemented custom fingerprints such as the all-shortest path fingerprint that only includes the subset of shortest paths from the full set of paths of the depth-first search fingerprint. As an application of jCompoundMapper, we provide a command-line executable binary. We measured the conversion speed and number of features for each encoding and described the composition of the features in detail. The quality of the encodings was tested using the default parametrizations in combination with a support vector machine on the Sutherland QSAR data sets. Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine. The results were promising and could often compete with literature results. On the large Ames benchmark, for example, we obtained an AUC ROC performance of 0.87 with a reimplementation of the extended connectivity fingerprint. This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors. On the Sutherland QSAR data set, the best fingerprint encodings showed a comparable or better performance on 5 of the 8 benchmarks when compared against the results of the best descriptors published in the paper of Sutherland et al. jCompoundMapper is a library for chemical graph fingerprints with several tweaking possibilities and exporting options for open source data mining toolkits. The quality of the data mining results, the conversion speed, the LPGL software license, the command-line interface, and the exporters should be useful for many applications in cheminformatics like benchmarks against literature methods, comparison of data mining algorithms, similarity searching, and similarity-based data mining.
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DOI：
10.1021/ci025584y
发表时间：
2003-03
期刊：
Journal of chemical information and computer sciences
影响因子：
--
作者：
Steinbeck C;Han Y;Kuhn S;Horlacher O;Luttmann E;Willighagen E
通讯作者：
Willighagen E
DOI：
10.1021/ci0498719
发表时间：
2004-09-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Bender, A;Mussa, HY;Reiling, S
通讯作者：
Reiling, S
影响因子：
5.6
作者：
Fechner, Nikolas;Jahn, Andreas;Zell, Andreas
通讯作者：
Zell, Andreas
影响因子：
7.8
作者：
Ralaivola, L;Swamidass, SJ;Baldi, P
通讯作者：
Baldi, P
影响因子：
5.6
作者：
Hansen, Katja;Mika, Sebastian;Mueller, Klaus-Robert
通讯作者：
Mueller, Klaus-Robert