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Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques

Author(s):

Juan Alberto Castillo-Garit*, Naivi Flores-Balmaseda, Orlando Álvarez, Hai Pham-The, Virginia Pérez-Doñate, Francisco Torrens and Facundo Pérez-Giménez   Pages 1 - 7 ( 7 )

Abstract:


Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models. The cutoff value to consider a compound as active one was IC50≤1.5μM. For this study, we employed Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning (ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The models developed with k-nearest neighbors and classification trees showed sensitivity values of 97% and 100%, respectively; while the models developed with artificial neural networks and support vector machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an external test-set was evaluated with good behavior for all models. A virtual screening was performed and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods to find new chemical compounds with anti-leishmanial activity.

Keywords:

Anti-leishmanial activity, Chemical compound, L. amazonensis, ML-based model, Virtual screening

Affiliation:

Experimental Toxicology Unit, Medical Sciences University of Villa Clara, Santa Clara, 50200, CAMD-BIR Unit, Chemistry-Pharmacy Faculty, Universidad Central de Las Villas, Santa Clara, 54830, CAMD-BIR Unit, Chemistry-Pharmacy Faculty, Universidad Central de Las Villas, Santa Clara, 54830, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Departamento de Microbiología. Hospital Universitario de la Ribera, Valencia, Institut Universitari de Ciència Molecular, Universitat de València, 46071, València, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València



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