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 Asteraceae that have presented a wide range of biological activities. From the ChEMBL database, we selected a diverse set of 4,452, 1,635 and 1,322 structures with tested activity against the three T. cruzi parasitic forms, amastigote, trypomastigotes and epimastigote, respectively, to create random forest (RF) models with an accuracy of greater than 74 % for cross-validation and test sets. Afterwards, a ligand-based virtual screen of the entire SLs of Asteraceae database stored in SistematX (1,306 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.
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A Combined Approach of Ligand-based and Structure-based Virtual Screening to select structures with potential antichagasic activity from SISTEMATX sesquiterpene lactones database.
Published: 06 December 2017 by MDPI in MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed. congress NICEXSM-03: North-Ibero-American Congress on Exp. & Simul. Methods, Valencia, Bilbao, Spain-Paraiba, Brasil-Miami, USA, 2017
Keywords: Asteraceae, Chagas disease, Ligand-based virtual screening, Structure-based virtual screening, Sesquiterpene lactones, Machine learning