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Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases
Author(s) -
Brenk Ruth,
Schipani Alessandro,
James Daniel,
Krasowski Agata,
Gilbert Ian Hugh,
Frearson Julie,
Wyatt Paul Graham
Publication year - 2008
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.200700139
Subject(s) - drug discovery , in silico , selection (genetic algorithm) , virtual screening , computer science , computational biology , chemical space , chemical library , combinatorial chemistry , data science , bioinformatics , biology , small molecule , chemistry , artificial intelligence , biochemistry , gene
To enable the establishment of a drug discovery operation for neglected diseases, out of 2.3 million commercially available compounds 222 552 compounds were selected for an in silico library, 57 438 for a diverse general screening library, and 1 697 compounds for a focused kinase set. Compiling these libraries required a robust strategy for compound selection. Rules for unwanted groups were defined and selection criteria to enrich for lead‐like compounds which facilitate straightforward structure–activity relationship exploration were established. Further, a literature and patent review was undertaken to extract key recognition elements of kinase inhibitors (“core fragments”) to assemble a focused library for hit discovery for kinases. Computational and experimental characterisation of the general screening library revealed that the selected compounds 1) span a broad range of lead‐like space, 2) show a high degree of structural integrity and purity, and 3) demonstrate appropriate solubility for the purposes of biochemical screening. The implications of this study for compound selection, especially in an academic environment with limited resources, are considered.