Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/4930
Title: Chemical space and Chemical diversity profiling of drug-like compounds from African natural product databases for Fragment based-drug discovery: Biodiversity for Malawi 2063: Research Dissemination Conference 20th – 22th July 2021 Mulanje, Malawi
Authors: Bvunzawabaya, Jonathan T
Mtewa, Andrew
Lampiao, Fanuel
Mugumbate, Grace
Keywords: Natural products and drug discovery
in silico drug discovery
African natural product databases
drug-like NPs
Issue Date: 2021
Abstract: Natural products (NPs) have been at the centre of attention in drug discovery. For the past decade, several NPs databases from the African continent have been compiled and made available to encourage in silico drug discovery. The aim of this work was to characterize the chemical diversity of drug-like NPs from four African natural product databases. 11304 compounds from four African natural products libraries were curated and filtered to remove structural alerts and compounds that violate drug-like rules according to Lipinski and Veber. The distribution of pharmacologically relevant properties of final 437 drug-like compounds were statistically analyzed, whereas their chemical space and diversity was characterized relative to 730 FDA drugs. Out of 11304 NPs, 30% were filtered as non-druglike, 11% as toxiphores and 56% as duplicates and ambiguous structures. The final drug-like dataset of compounds contained more than 60% of its NPs with lead-like character. Chemical diversity analysis revealed that NPs more diversity than FDA drugs scaffolds wise but not in terms of structural fingerprints. These findings highlight that the African NP databases contain drug-like NPs that are chemically diverse and hence provide promising and unique scaffolds for fragment-based drug designing
URI: https://www.researchgate.net/publication/354727575_Chemical_space_and_Chemical_diversity_profiling_of_drug-like_compounds_from_African_natural_product_databases_for_Fragment_based-drug_discovery
http://hdl.handle.net/11408/4930
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