The CARLSBAD database: a confederated database of chemical bioactivities.
The CARLSBAD database: a confederated database of chemical bioactivities.
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DOI：
10.1093/database/bat044
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Oprea TI
中科院分区：
文献类型：
--
作者：
Mathias SL;Hines-Kay J;Yang JJ;Zahoransky-Kohalmi G;Bologa CG;Ursu O;Oprea TI
许多生物活性数据库提供了关于小分子在蛋白质靶点上的生物活性的信息。这些数据库中的信息往往难以确定地解决，因为以各种格式细分不同的数据；使用不同的生物活性指标；对化学品和蛋白质使用不同的标识符；和必须分别访问不同的查询接口。考虑到大量的数据来源、接口和标准，收集相关事实并做出有关化学-蛋白质关联的适当联系和决策是具有挑战性的。CARLSBAD数据库是一个集成资源，集中了来自多个生物活性数据库的高质量子集，这些子集以统一的方式聚集和呈现，适合研究小分子与靶点之间的关系。与数据收集资源相比，CARLSBAD为每个独特的化学蛋白靶对提供给定类型的单一标准化活性值。两种类型的支架感知方法已经实现并可用于数据挖掘：HierS（分层支架）和MCES（最大公共边子图）。2012年发布的carsbad包含439985个独特的化学结构，映射到1420889个独特的生物活性，分别注释了277 140个HierS支架和54 135个MCES化学模式。在CARLSBAD中筛选到的890323对独特的结构-靶标对中，13.95%是由多个结构-靶标值聚合而来的，其中94975对是由两个生物活性聚合而来，14544对是由三个生物活性聚合而来，7930对是由四个生物活性聚合而来，2214对是由五个生物活性聚合而来。CARLSBAD捕获1435种活性药物成分（即“药物”）的独特化学结构的生物活性和标签。CARLSBAD处理导致化学物质数据净减少17.3%，生物活性数据净减少34.3%，HierS数据净减少23%，MCES数据净减少25%。CARLSBAD数据库支持一个知识挖掘系统，该系统为非专业人员提供了探索化学生物学空间的新颖综合方法，以促进药物发现和再利用中的知识挖掘。数据库地址：http://carlsbad.health.unm.edu/carlsbad/。
Many bioactivity databases offer information regarding the biological activity of small molecules on protein targets. Information in these databases is often hard to resolve with certainty because of subsetting different data in a variety of formats; use of different bioactivity metrics; use of different identifiers for chemicals and proteins; and having to access different query interfaces, respectively. Given the multitude of data sources, interfaces and standards, it is challenging to gather relevant facts and make appropriate connections and decisions regarding chemical–protein associations. The CARLSBAD database has been developed as an integrated resource, focused on high-quality subsets from several bioactivity databases, which are aggregated and presented in a uniform manner, suitable for the study of the relationships between small molecules and targets. In contrast to data collection resources, CARLSBAD provides a single normalized activity value of a given type for each unique chemical–protein target pair. Two types of scaffold perception methods have been implemented and are available for datamining: HierS (hierarchical scaffolds) and MCES (maximum common edge subgraph). The 2012 release of CARLSBAD contains 439 985 unique chemical structures, mapped onto 1,420 889 unique bioactivities, and annotated with 277 140 HierS scaffolds and 54 135 MCES chemical patterns, respectively. Of the 890 323 unique structure–target pairs curated in CARLSBAD, 13.95% are aggregated from multiple structure–target values: 94 975 are aggregated from two bioactivities, 14 544 from three, 7 930 from four and 2214 have five bioactivities, respectively. CARLSBAD captures bioactivities and tags for 1435 unique chemical structures of active pharmaceutical ingredients (i.e. ‘drugs’). CARLSBAD processing resulted in a net 17.3% data reduction for chemicals, 34.3% reduction for bioactivities, 23% reduction for HierS and 25% reduction for MCES, respectively. The CARLSBAD database supports a knowledge mining system that provides non-specialists with novel integrative ways of exploring chemical biology space to facilitate knowledge mining in drug discovery and repurposing. Database URL: http://carlsbad.health.unm.edu/carlsbad/.
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影响因子：
14.9
作者：
Sharman JL;Benson HE;Pawson AJ;Lukito V;Mpamhanga CP;Bombail V;Davenport AP;Peters JA;Spedding M;Harmar AJ;NC-IUPHAR
通讯作者：
NC-IUPHAR
影响因子：
5.6
作者：
Gardiner, Eleanor J.;Gillet, Valerie J.;Cosgrove, David A.
通讯作者：
Cosgrove, David A.
影响因子：
5.6
作者：
Sheridan, RP;Hunt, P;Culberson, JC
通讯作者：
Culberson, JC
影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium
影响因子：
5.6
作者：
Hariharan, Ramesh;Janakiraman, Anand;Schuffenhauer, Ansgar
通讯作者：
Schuffenhauer, Ansgar