Machine learning techniques and the identification of new potentially active compounds against Leishmania infantum.

Abstract

Leishmaniasis is defined as a set of diseases of very varied clinical presentation produced by obligate intracellular parasites belonging to the genus Leishmania. They have been classified by the World Health Organization in category I of infectious diseases and are part of neglected tropical pathologies. Leishmania infantum mainly affects children under five years of age and has been associated with an increase in the appearance of cutaneous and visceral leishmaniasis. The search for new therapeutic alternatives remains a challenge and in silico studies are alternative tools to solve this problem. With the main objective of identify potentially effective compounds against Leishmania infantum through in silico studies, artificial Intelligence techniques implemented in the WEKA program and molecular descriptors 0D-2D of DRAGON software are used in this research. A new database was created and the clusters analysis (AC) k-means was used to design the training and prediction series. Four models were obtained with the following techniques: IBk, J48, MLP and SMO that reached percentages of classification higher than 80% for training and prediction series, whose predictive power was confirmed through external and internal validation procedures. The use of the models obtained in the virtual screening of the international database DrugBank and synthesis compounds allowed the optimal identification of 120 new potentially active compounds against Leishmania infantum amastigote form.