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Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome

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 co...

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Detalles Bibliográficos
Autores principales: Miljković, Filip, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Future Science Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695529/
https://www.ncbi.nlm.nih.gov/pubmed/31428450
http://dx.doi.org/10.2144/fsoa-2019-0040
Descripción
Sumario: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.