Laufkötter Oliver, Laufer Stefan, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53115, Germany.
Department of Pharmacy and Biochemistry, Pharmaceutical/Medicinal Chemistry, TüCADD (Tübingen Center for Academic Drug Discovery), Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, Tübingen D-72076, Germany.
Data Brief. 2020 Aug 15;32:106189. doi: 10.1016/j.dib.2020.106189. eCollection 2020 Oct.
A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.
我们收集了大量具有高可信度活性数据的多激酶抑制剂,并用于生成基于共享抑制剂揭示激酶关系的网络表示[1]。化合物和活性注释最初从公共存储库中选择,并整理到一个内部数据库中,从中提取并整理数据集。新数据集包含超过36000种抑制剂,对总共420种人类激酶具有多种活性注释(覆盖人类激酶组的81%),代表了总共约127000种激酶-抑制剂相互作用。该数据的使用不限于[1]中报道的网络应用。例如,它还可用于不同类型的化合物多效性分析或机器学习(如多任务建模)。此外,该数据集为利用外部化合物信息补充激酶药物发现项目提供了大量资源。