用于化合物多靶向性分析的数据结构：由人类激酶组抑制剂形成的多靶向性悬崖、通路和多靶向性枢纽。
Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome.
作者信息
Miljković Filip, Bajorath Jürgen
机构信息
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.
出版信息
Future Sci OA. 2019 Jul 25;5(7):FSO404. doi: 10.2144/fsoa-2019-0040.
AIM
A large collection of promiscuity cliffs (PCs), PC pathways (PCPs) and promiscuity hubs (PHs) formed by inhibitors of human kinases is made freely available.
METHODOLOGY
Inhibitor PCs were systematically identified and organized in network representations, from which PCPs were extracted. PH compounds were classified and their neighborhoods analyzed.
DATA & EXEMPLARY RESULTS: Nearly 16,000 PCs covering the human kinome were identified, which yielded more than 600 PC clusters and 8900 PCPs. Moreover, 520 PHs were obtained.
LIMITATIONS & NEXT STEPS: PC and PCP data structures capture structure-promiscuity relationships. Promiscuity assessment is also affected by data sparseness. Given the rapid growth of kinase inhibitor data, the relevance of PC/PCP/PH information for medicinal chemistry and chemical biology applications will further increase.
目的
免费提供由人类激酶抑制剂形成的大量混杂性悬崖（PCs）、PC途径（PCPs）和混杂性枢纽（PHs）。
方法
系统识别抑制剂PCs并以网络表示形式进行组织，从中提取PCPs。对PH化合物进行分类并分析其邻域。
数据与示例结果
识别出近16000个覆盖人类激酶组的PCs，产生了600多个PC簇和8900个PCPs。此外，获得了520个PHs。
局限性与下一步计划
PC和PCP数据结构捕获结构-混杂性关系。混杂性评估也受数据稀疏性影响。鉴于激酶抑制剂数据的快速增长，PC/PCP/PH信息在药物化学和化学生物学应用中的相关性将进一步提高。
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