Malaria causes millions of deaths every year and conventionally used drugs are generating resistance phenomena. Therefore, it is necessary to develop new antimalarial drugs urgently. In this work, classification and regression models are developed on a chemical set (Malaria Box) to find relations between molecular structures and their corresponding activities. In this study, the antimalarial activity (expressed as the EC50) of 317 chemical entities was modeled against the parasite Plasmodium falciparum. The chemical structures were encoded using discrete molecular graph derivative indices (GDIs). These were calculated using the DIVATI module of the TOMOCOMD-CARDD software. Later, to select descriptors with greater variability, based on the calculation of the Shannon’s entropy, the IMMAN software was used. Classification models was developed with WEKA software, showing SMO technique, statistical superiority. The RLM models were found with the MobyDigs software. Nonlinear regression models were found with the WEKA software, using Support Vector Machines. The results of regression modeling suggest statistical superiority of the nonlinear model with respect to the linear model, using the same variables, which establish the bases to predict the antimalarial activity using the GDIs, classification and the nonlinear regression technique, for further studies of virtual screening and proposal of antimalarial candidates.

Malaria causes millions of deaths every year and conventionally used drugs are generating resistance phenomena. Therefore, it is necessary to develop new antimalarial drugs urgently. In this work, classification and regression models are developed on a chemical set (Malaria Box) to find relations between molecular structures and their corresponding activities. In this study, the antimalarial activity (expressed as the EC50) of 317 chemical entities was modeled against the parasite _Plasmodium falciparum_. The chemical structures were encoded using discrete molecular graph derivative indices (GDIs). These were calculated using the DIVATI module of the TOMOCOMD-CARDD software. Later, to select descriptors with greater variability, based on the calculation of the Shannon’s entropy, the IMMAN software was used. Classification models was developed with WEKA software, showing SMO technique, statistical superiority. The RLM models were found with the MobyDigs software. Nonlinear regression models were found with the WEKA software, using Support Vector Machines. The results of regression modeling suggest statistical superiority of the nonlinear model with respect to the linear model, using the same variables, which establish the bases to predict the antimalarial activity using the GDIs, classification and the nonlinear regression technique, for further studies of virtual screening and proposal of antimalarial candidates.

QSAR Modeling for the Antimalarial Activity of organic compounds using Discrete Derivative as Molecular Descriptors

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