The biomineral haemozoin, or its synthetic analogue β-haematin (βH), has been the focus of several target-based screens for activity against Plasmodium falciparum parasites. Together with the known βH crystal structure, the availability of this screening data makes the target amenable to both structure-based and ligand-based virtual screening. In this study, molecular docking and machine learning techniques, including Bayesian and support vector machine classifiers, were used in sequence to screen the in silico ChemDiv 300k Representative Compounds library for inhibitors of βH with retained activity against P. falciparum . We commercially obtained and tested a prioritised set of inhibitors and identified the coumarin and iminodipyridinopyrimidine chemotypes as potent in vitro inhibitors of βH and whole cell parasite growth.
【저자키워드】 Drug discovery, structural biology, Computational biology and bioinformatics, Chemical biology, Target validation, Screening, Cell Biology, Chemistry, Medicinal chemistry, Cheminformatics, Drug discovery and development, Chemical libraries, Inorganic chemistry,