ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.
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DOI：
10.1186/s13321-016-0174-y
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发表时间：
2016
影响因子：
8.6
通讯作者：
Wishart DS
中科院分区：
文献类型：
--
作者：
Djoumbou Feunang Y;Eisner R;Knox C;Chepelev L;Hastings J;Owen G;Fahy E;Steinbeck C;Subramanian S;Bolton E;Greiner R;Wishart DS
长期以来，科学家一直渴望使用分类法和/或本体来描述、组织、分类和比较对象。与生物学、地质学和许多其他科学学科相比，化学世界仍然缺乏标准化的化学本体论或分类学。在化学分类方面已经进行了几次尝试;但它们大多限于手动或半自动原理证明应用。这是令人遗憾的，因为全面的化学分类和描述工具不仅可以提高我们对化学的理解，而且可以改善化学与许多其他领域之间的联系。例如，化合物的化学分类可以帮助预测其在人类中的代谢命运，其可药用性或与之相关的潜在危害等。然而，化学结构的绝对数量（数千万种化合物）和复杂性使得任何手动分类工作几乎是不可能的。我们已经开发了一个全面的，灵活的，可计算的，纯粹基于结构的化学分类法（ChemOnt），沿着一个计算机程序（ClassyFire），它只使用化学结构和结构特征来自动将所有已知的化合物分配到一个由>4800个不同类别组成的分类法中。这种新的化学分类法包括多达11个不同的级别（王国，总类，类，子类等）。每个类别都由明确的、可计算的结构规则定义。此外，每个类别都使用基于共识的命名法命名，并根据其所含化合物的特征共同结构特性进行描述（英语）。ClassyFire Web服务器可在http://classyfire.wishartlab.com/上免费访问。此外，Ruby API版本可在https：//bitbucket.org/wishartlab/classyfire_API上获得，它提供对ClassyFire服务器和数据库的编程访问。ClassyFire已被用于注释超过7700万种化合物，并已被集成到其他软件包中，以自动生成超过10万种化合物的文本描述和/或推断其生物学特性。本文提供了其他的例子和应用。ClassyFire与ChemOnt（ClassyFire的综合化学分类法）相结合，现在允许化学家和化学信息学家进行大规模，快速和自动化的化学分类。此外，一个免费访问的API允许轻松访问超过7700万个“ClassyFire”分类化合物。这些结果可以用来帮助注释研究得很好的化合物，以及鲜为人知的化合物。此外，这些化学分类可用作数据集成和许多其他化学信息学相关任务的输入。本文的在线版本（doi：10.1186/s13321-016-0174-y）包含补充材料，可供授权用户使用。
Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper. ClassyFire, in combination with ChemOnt (ClassyFire’s comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million “ClassyFire” classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks. The online version of this article (doi:10.1186/s13321-016-0174-y) contains supplementary material, which is available to authorized users.
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影响因子：
14.9
作者：
Hastings J;de Matos P;Dekker A;Ennis M;Harsha B;Kale N;Muthukrishnan V;Owen G;Turner S;Williams M;Steinbeck C
通讯作者：
Steinbeck C
影响因子：
14.9
作者：
Kim S;Thiessen PA;Bolton EE;Chen J;Fu G;Gindulyte A;Han L;He J;He S;Shoemaker BA;Wang J;Yu B;Zhang J;Bryant SH
通讯作者：
Bryant SH
影响因子：
8.6
作者：
Hastings, Janna;Magka, Despoina;Steinbeck, Christoph
通讯作者：
Steinbeck, Christoph
DOI：
10.1007/978-1-60761-839-3_10
发表时间：
2011-01-01
期刊：
CHEMOINFORMATICS AND COMPUTATIONAL CHEMICAL BIOLOGY
影响因子：
--
作者：
Ertl, Peter;Schuffenhauer, Ansgar;Renner, Steffen
通讯作者：
Renner, Steffen
影响因子：
9.5
作者：
Hoehndorf R;Schofield PN;Gkoutos GV
通讯作者：
Gkoutos GV