Ensemble-based modeling of chemical compounds with antimalarial activity.

Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure-Activity Relationship studies have been widely used for drug design and they stand as an alternative to traditional drug discovery. The main goal of this research is to develop computational models for the identification of antimalaric hit compounds. For this, a data set suitable for the modeling of the anti-malaric activity of chemical compounds was compiled from the literature and subject to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalaric hits based on its virtual screening performance. During the compilation of the data set it was possible to obtain a high quality data which was curated to ensure that the lower noise as possible would affect the modeling process. Among the explored ensemble based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential anti-malarial compounds.

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