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

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.

Identificación de compuestos líderes con potencial actividad antileishmaniásica: una contribución a la farmacéutica médica / Identification of lead compounds with potential antileishmanial activity: a contribution to pharmaceutical medicine

Introducción: la leishmaniasis es una enfermedad provocada por protozoos del género Leishmania, cuyas repercusiones pueden ser fatales o provocar discapacidad. En el presente trabajo se identifican nuevos compuestos con potencial actividad antileishmaniásica empleando estudios in silico. Objetivo: confeccionar una nueva base de datos de compuestos evaluados contra este parásito e identificar nuevos compuestos líderes con potencial actividad antileishmaniásica a través de estudios in silico. Métodos: los compuestos incluidos se recopilaron de la literatura y bases de datos internacionales, tomando la concentración inhibitoria media como medida de clasificación en activos e inactivos. Se emplearon los softwares DRAGON y STATISTICA 8.0 para calcular los descriptores moleculares;el análisis de clúster y la distancia euclidiana se emplearon en la confección de las series de entrenamiento y predicción. Los modelos fueron validados, y se realizó una búsqueda de nuevos compuestos líderes a través del tamizaje virtual de bases de datos(AU)