Abstract
Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.
Highlights
Natural products (NP) have long been studied and used in medicine and chemistry, starting from ancient civilizations throughout history
Considering the structural complexity of NP, it is a challenge to produce them in large quantities, which is typically required during drug development
A recent notable example is the COlleCtion of Open NatUral producTs (COCONUT), a compendium of 50 open-access databases collecting more than 400,000 compounds
Summary
Natural products (NP) have long been studied and used in medicine and chemistry, starting from ancient civilizations throughout history. The increasing use of NP in modern drug discovery has promoted the application of chemoinformatic methods for natural product-based drug discovery. One such contribution is the generation and development of compound databases [6–8]. A recent notable example is the COlleCtion of Open NatUral producTs (COCONUT), a compendium of 50 open-access databases collecting more than 400,000 compounds These and other public collections of food chemicals are important sources to generate fragment libraries of compounds of natural origin. We expanded the analysis to generate fragment libraries of large public collections of 23,883 food chemicals that have a close association with NP [11] and are part of the increasing research field of foodinformatics [12]. Food chemicals and DCM compounds analyzed in this work were recently screened in silico to identify potential inhibitors of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), one of the main promising molecular targets for the treatment of COVID-19 [15]
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