Center for Bioinformatics, University of Kansas, Lawrence, KS 66045, USA.
BMC Bioinformatics. 2011;12 Suppl 5(Suppl 5):S1. doi: 10.1186/1471-2105-12-S5-S1. Epub 2011 Jul 27.
Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the "undruggable" part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem.
In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy.
Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery.
尽管投资增长和技术发展迅猛，但在过去十年中，药物发现和开发仍存在明显的瓶颈。NIH 于 2003 年启动了分子图书馆倡议（MLI），以扩大潜在药物靶点的库，特别是来自人类基因组“不可成药”部分的靶点，以及来自更广泛类型的类药小分子的潜在药物候选物。所有结果都在一个名为 PubChem 的网络门户中公开提供。
在本文中，我们从 PubChem 中的生物测定数据构建了一个网络，应用网络生物学概念来描述这个生物测定网络，整合来自多个生物数据库（例如 DrugBank、OMIM 和 UniHI）的信息，并系统地分析生物测定靶点作为新的药物靶点的潜力在复杂的生物网络背景下。我们提出了一个模型来定量优先考虑生物测定靶点的可成药性，并找到了文献证据来证实我们对生物测定靶点的优先级排序的准确性约为 70%。
我们的分析提供了一些衡量 MLI 数据作为基础化学生物学研究和未来治疗发现资源的价值的指标。
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