Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases

By R. Brenk, A. Schipani, Daniel James, A. Krasowski, I. Gilbert, J. Frearson, P. Wyatt. ChemMedChem, 2007

635 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/544b24aff4b72f81d1ad41ae4d368f8922209f14

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In Computational Theory and Mathematics

In Organic Chemistry

Citations in the corpus

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules (2017, neutral): “Besides, we implemented Structural Alert, which consists in a list of 105 fragments identified by Brenk et al. 5 to be putatively toxic, chemically reactive, metabolically unstable or to bear properties responsible for poor pharmacokinetics.”
Discovery of a structural class of antibiotics with explainable deep learning (2023, background): “The PAINS and Brenk alerts 30,31 refer to chemical substructures that may be promiscuous or toxic.”
The ChEMBL database in 2017 (2016, methodology): “pdf), University of Dundee NTD Screening Library Filters (45) and Pan Assay Interference Compounds (PAINS) Filters (46), filters derived by Inpharmatica Ltd, set of alerts currently used in SureChEMBL https://www.”
Computational Methods in Drug Discovery (2014, background): “of structural alerts (Brenk et al., 2008).”