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

Abstract

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.

Comparison of a High-Throughput High-Content Intracellular Leishmania donovani Assay with an Axenic Amastigote Assay

“…The small-molecule library used in the present study contained 15,659 diverse compounds, dissolved at 10 mM in 100% dimethyl sulfoxide (DMSO). This library was assembled using the criteria for a diverse compound collection outlined elsewhere (28) and stored under low-oxygen and lowhumidity conditions. Cells and cell lines.…”

Computational Methods in Drug Discovery

“…This score is taken as a geometric mean of individual desirability functions, each of which corresponds to a different molecular descriptor. These descriptors include molecular weight, logP, hydrogen bond donors and acceptors, rotatable bonds, aromatic rings, and the number of structural alerts (Brenk et al, 2008).…”

Virtual Screening Strategies in Drug Design

“…20 Dessa forma, o objetivo principal de uma triagem virtual é o de identificar os compostos de uma biblioteca que tenham maior probabilidade de se ligar a um alvo biológico. Com os avanços tecnológicos, a cada dia fica mais palpável a realização e aplicação prática dos conceitos de 33,34 propriedades estas que representam o principal gargalo no insucesso de candidatos a fármacos.…”