Bayer Pharma AG, Müllerstr 178, 13342 Berlin, Germany.
BMC Bioinformatics. 2014 Mar 11;15:68. doi: 10.1186/1471-2105-15-68.
Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats.
We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface.
Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.
药物-靶标关系的信息是药物发现的核心。现在有几十个数据库提供具有不同范围和重点的药物-靶标相互作用数据。因此，由于化学空间很大，不同数据集的重叠非常小。由于手动搜索这些来源既繁琐、耗时又容易出错，因此非常需要整合所有数据。尽管有一些尝试，但由于化合物描述的多样性以及由于来自不同数据集的报告活性值由于使用不同的指标或数据格式而并不总是直接可比，因此整合一直受到阻碍。
我们构建了 Drug2Gene，这是一个知识库，它结合了来自 19 个公开可用数据库的化合物/药物-基因/蛋白质信息。一个关键特点是我们严格的统一和标准化流程，使数据在大规模上真正具有可比性，从而首次能够在如此大的知识语料库中进行有效的数据挖掘。截至版本 3.2，Drug2Gene 包含 4,372,290 个化合物与其靶标之间的统一关系，其中大多数关系都包含报告的生物活性数据。我们通过足够的蛋白质序列同源性暗示它们可能与类似的化合物结合的假定（即同源推断）关系扩展了这个集合。Drug2Gene 提供了强大的搜索功能、非常灵活的导出程序和用户友好的 Web 界面。
Drug2Gene v3.2 已成为一个成熟且全面的知识库，提供了来自公开数据源的统一、标准化的药物-靶标相关信息。它可用于将专有数据集与公开数据集进行整合。它的主要目标是成为一个“一站式”商店，用于识别针对给定基因产物的工具化合物，或找到已知药物的所有已知靶标。Drug2Gene 及其集成的公共化合物-靶标关系数据集可在无限制的情况下免费访问，网址为 http://www.drug2gene.com。
