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Article Abstract

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti- potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of . The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of , showing IC values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the analyses, we can conclude that andrographolide fulfills many requirements implemented by DND to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of .

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http://dx.doi.org/10.1021/acs.jcim.3c01410DOI Listing

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