Premium
Toward an Efficient Approach to Identify Molecular Scaffolds Possessing Selective or Promiscuous Compounds
Author(s) -
Yongye Austin B.,
MedinaFranco José L.
Publication year - 2013
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12162
Subject(s) - scaffold , drug discovery , computational biology , computer science , small molecule , identification (biology) , chemical space , set (abstract data type) , tree (set theory) , combinatorial chemistry , data mining , chemistry , bioinformatics , biology , biochemistry , mathematics , database , mathematical analysis , botany , programming language
The concept of a recurrent scaffold present in a series of structures is common in medicinal drug discovery. We present a scaffold analysis of compounds screened across 100 sequence‐unrelated proteins to identify scaffolds that drive promiscuity or selectivity. Selectivity and promiscuity play a major role in traditional and poly‐pharmacological drug design considerations. The collection employed here is the first publicly available data set containing the complete screening profiles of more than 15 000 compounds from different sources. In addition, no scaffold analysis of this data set has been reported. The protocol described here employs the Molecular Equivalence Index tool to facilitate the selection of B emis– M urcko frameworks in the data set, which contain at least five compounds and S caffold H unter to generate a hierarchical tree of scaffolds. The annotation of the scaffold tree with protein‐binding profile data enabled the successful identification of mostly highly specific compounds, due to data set constraints. We also applied this approach to a public set of 1497 small molecules screened non‐uniformly across a panel of 172 protein kinases. The approach is general and can be applied to any other data sets and activity readout.