SuperToxic: a comprehensive database of toxic compounds.

SuperToxic: a comprehensive database of toxic compounds.
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DOI:
10.1093/nar/gkn850
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发表时间:
2009-01
影响因子:
14.9
通讯作者:
Preissner R
Preissner R
中科院分区:
生物学2区
文献类型:
--
作者:
Schmidt U;Struck S;Gruening B;Hossbach J;Jaeger IS;Parol R;Lindequist U;Teuscher E;Preissner R

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在我们的日常生活中,我们面临着各种天然或人造的有毒物质。毒素已经被使用,例如但有毒化合物的数量仍在不断增加,这代表着提取新物质的巨大潜力。由于预测毒理学变得越来越重要,对已知毒素的仔细和广泛的研究是评估未知物质特性的基础。为了实现这一目标,我们从文献和网络资源中收集了有毒化合物,并将其存储在数据库 SuperToxic 中。该数据库的当前版本编译了大约 60 000 种化合物及其结构。这些分子根据超过 200 万次测量的毒性进行分类。 SuperToxic 数据库提供多种搜索选项,如名称、CASRN、分子量和毒性测量值。借助实施的相似性搜索,可以获得有关可能的生物相互作用的信息。此外,还可以连接到蛋白质数据库、UniProt 和 KEGG 数据库,以便识别所搜索的化合物所涉及的靶标和途径。该数据库可在线获取:http://bioinformatics.charite.de/superknown。
Within our everyday life, we are confronted with a variety of toxic substances of natural or artificial origin. Toxins are already used, e.g. in medicine, but there is still an increasing number of toxic compounds, representing a tremendous potential to extract new substances. Since predictive toxicology gains in importance, the careful and extensive investigation of known toxins is the basis to assess the properties of unknown substances. In order to achieve this aim, we have collected toxic compounds from literature and web sources in the database SuperToxic. The current version of this database compiles about 60 000 compounds and their structures. These molecules are classified according to their toxicity, based on more than 2 million measurements. The SuperToxic database provides a variety of search options like name, CASRN, molecular weight and measured values of toxicity. With the aid of implemented similarity searches, information about possible biological interactions can be gained. Furthermore, connections to the Protein Data Bank, UniProt and the KEGG database are available, to allow the identification of targets and those pathways, the searched compounds are involved in. This database is available online at: http://bioinformatics.charite.de/supertoxic.
CEBS - 生物系统的化学效应:一个公共数据存储库将研究设计和毒性数据与微阵列和蛋白质组学数据相结合。
DOI: 10.1093/nar/gkm755
发表时间: 2008-01
影响因子: 14.9
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影响因子: 14.9
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通讯作者: Fostel, Jennifer M.