| Literature DB >> 29323468 |
Chonny Herrera Acevedo1, Luciana Scotti1, Marcus Tullius Scotti1.
Abstract
Chagas disease is an endemic disease caused by Trypanosoma cruzi, which affects more than eight million people, mostly in the Americas. A search for new treatments is necessary to control and eliminate this disease. Sesquiterpene lactones (SLs) are an interesting group of secondary metabolites characteristic of the Asteraceae family that have presented a wide range of biological activities. From the ChEMBL database, we selected a diverse set of 4452, 1635, and 1322 structures with tested activity against the three T. cruzi parasitic forms: amastigote, trypomastigote, and epimastigote, respectively, to create random forest (RF) models with an accuracy of greater than 74 % for cross-validation and test sets. Afterward, a ligand-based virtual screen of the entire SLs of the Asteraceae database stored in SistematX (1306 structures) was performed. In addition, a structure-based virtual screen was also performed for the same set of SLs using molecular docking. Finally, using an approach combining ligand-based and structure-based virtual screening along with the equations proposed in this study to normalize the probability scores, we verified potentially active compounds and established a possible mechanism of action.Entities:
Keywords: Chagas disease; asteraceae; machine learning; sesquiterpene lactones; virtual screening
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Year: 2018 PMID: 29323468 DOI: 10.1002/cmdc.201700743
Source DB: PubMed Journal: ChemMedChem ISSN: 1860-7179 Impact factor: 3.466