Structure-based classification and ontology in chemistry
Structure-based classification and ontology in chemistry
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DOI：
10.1186/1758-2946-4-8
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发表时间：
2012-04-05
影响因子：
8.6
通讯作者：
Steinbeck, Christoph
中科院分区：
文献类型：
--
作者：
Hastings, Janna;Magka, Despoina;Steinbeck, Christoph
背景：近年来，化学领域的数据可用性呈爆炸式增长。然而，随着信息爆炸，从可用信息中检索相关结果并组织这些结果变得更加困难。计算处理对于过滤和组织可用资源至关重要，以便更好地促进科学家的工作。本体编码专家领域知识的分层组织的机器可处理的格式。化学领域的一个这样的本体是ChEBI。ChEBI根据化学品的结构特征和作用或活性进行分类。基于结构的类别的一个例子是“五环化合物”（含有五环结构的化合物），而基于角色的类别的一个例子是“镇痛剂”，因为许多不同的化学品可以作为镇痛剂而不共享结构特征。化学中基于结构的分类利用了潜在化学领域中优雅的对称性和对称性。到目前为止，既没有一个系统的分析，在化学中使用的结构分类的类型，也没有一个比较的能力，可用technology.Results：我们分析了不同类别的结构类在化学中，提出了一个列表中的模式中发现的功能类定义。我们比较这些模式的类定义的工具，允许自动化的层次结构建设内的化学信息学和基于逻辑的本体技术，在后一种情况下详细的表达能力的Web本体语言和最近的扩展建模结构化对象。最后，我们讨论了化学信息学方法和基于逻辑的approaches.Conclusion之间的关系和相互作用：系统执行智能推理任务的化学数据需要一个不同的底层计算工具，包括算法，统计和基于逻辑的工具。对于基于结构的化学实体自动分类任务，管理大量在线化学数据至关重要，能够结合几种不同方法进行混合推理的系统至关重要。我们提供了可用的工具和方法的彻底审查，并确定开放研究的领域。
Background: Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.Results: We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.Conclusion: Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.