Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree.

Abstract:

:Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease.

journal_name

SAR QSAR Environ Res

authors

Castillo-Garit JA,Barigye SJ,Pham-The H,Pérez-Doñate V,Torrens F,Pérez-Giménez F

doi

10.1080/1062936X.2020.1863857

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

71-83

issue

1

eissn

1062-936X

issn

1029-046X

journal_volume

32

pub_type

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