Capturing antibacterial natural products with in silico techniques

Molecular Medicine Reports
Mahmud MasalhaAnwar Rayan

Abstract

The aim of the present study was to index natural products in order to facilitate the discovery of less expensive antibacterial therapeutic drugs. Thus, for modeling purposes, the present study utilized a set of 628 antibacterial drugs, representing the active domain, and 2,892 natural products, representing the inactive domain. In addition, using the iterative stochastic elimination algorithm, 36 unique filters were identified, which were then used to construct a highly discriminative and robust model tailored to index natural products for their antibacterial bioactivity. The area attained under the curve was 0.957, indicating a highly discriminative and robust prediction model. Utilizing the proposed model to virtually screen a mixed set of active and inactive substances enabled the present study to capture 72% of the antibacterial drugs in the top 1% of the sample, yielding an enrichment factor of 72. In total, 10 natural products that scored highly as antibacterial drug candidates with the proposed indexing model were reported. PubMed searches revealed that 2 molecules out of the 10 (caffeine and ricinine) have been tested and identified as showing antibacterial activity. The other 8 phytochemicals await experimental evalua...Continue Reading

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Citations

Sep 12, 2019·Frontiers in Pharmacology·Yan A IvanenkovOlga A Dontsova
Sep 5, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Vika GabeAnwar Rayan
Aug 23, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Vika GabeAnwar Rayan

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