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.
Herein we present results of a quantitative structure-activity relationship (QSAR) study to identify new antileishmaniasic compounds (Leishmania amazonensis) by using a set of more than 2000 0D-2D Dragon´s molecular descriptors and machine learning techniques. A data set of organic chemicals, with antileishmaniasic activity against promastigote forms of the parasite, is used to develop four QSAR models based on k-nearest neighbors, Support Vector Machine, Multi-Layer Perceptron and classification tree techniques. External validation procedures were developed to demonstrate the predictive power of the models. Promastigote´s models correctly classify more than 89% chemicals in both training and external prediction groups, respectively. In addition to the individual techniques an assembled system of majority vote was personalized with the aim of improving the results of the obtained models. To identify new compounds with potential activity against this parasite, a virtual screening was performed using DrugBank international database. There were identified more than five hundred new potential antileishmaniasic compounds. The current results constitute a step forward in the search for efficient ways to discover new antileishmaniasic lead compounds.
Leishmaniasis is one of the most important neglected tropical diseases according to the World Health Organization. The available drugs are expensive, not sufficiently effective, have serious cytotoxic effects and parasitic resistance has increased in the last years. In the present MOL2NET, 2022, 7,
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