Chemical Space: Big Data Challenge for Molecular Diversity
Chemical Space: Big Data Challenge for Molecular Diversity
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DOI：
10.2533/chimia.2017.661
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发表时间：
2017-01-01
期刊：
影响因子：
1.2
通讯作者：
Reymond, Jean-Louis
中科院分区：
文献类型：
--
作者：
Awale, Mahendra;Visini, Ricardo;Reymond, Jean-Louis
化学空间描述了所有可能的分子以及代表这些分子结构多样性的多维概念空间。这种化学空间的一部分可以在公共数据库中找到，范围从数千到数十亿种化合物。利用这些数据库进行药物发现是一个典型的大数据问题，受到计算能力、数据存储和数据访问能力的限制。在这里，我们回顾了我们实验室的最新进展，包括化学宇宙数据库（GDB）和片段子集FDB-17的进展，最近邻搜索的基于配体的虚拟筛选工具，如我们的多指纹浏览器的ZINC数据库，以选择可购买的筛选化合物，以及它们在发现钙通道TRPV 6和Aurora A激酶的有效和选择性抑制剂中的应用，用于预测脱靶效应的多药理学浏览器（PPB），以及使用我们的在线工具WebDrugCS和WebMolCS的交互式3D化学空间可视化。本文所述的所有资源均可在www.gdb.unibe.ch上公开使用。
Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases for drug discovery represents a typical big data problem limited by computational power, data storage and data access capacity. Here we review recent developments of our laboratory, including progress in the chemical universe databases (GDB) and the fragment subset FDB-17, tools for ligand-based virtual screening by nearest neighbor searches, such as our multi-fingerprint browser for the ZINC database to select purchasable screening compounds, and their application to discover potent and selective inhibitors for calcium channel TRPV6 and Aurora A kinase, the polypharmacology browser (PPB) for predicting off-target effects, and finally interactive 3D-chemical space visualization using our online tools WebDrugCS and WebMolCS. All resources described in this paper are available for public use at www.gdb.unibe.ch.