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.

KEYWORDS:

• Anti-chagasic action
• Chagas disease
• classification tree
• Trypanosoma cruzi
• virtual screening

Acknowledgements

Castillo-Garit, J.A. thanks the programme ‘Estades Temporals per a Investigators Convidats’ for a fellowship to work at Valencia University in 2018. Also, F. Torrens acknowledges financial support from Fundación Universidad Católica de Valencia San Vicente Mártir (Project No. 2019-217-001UCV).

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplementary data for this article can be accessed at https://doi.org/10.1080/1062936X.2020.1863857.