Quantitative structure-activity relationship for antimalarial activity of artemisinin

Abstract

The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity. Quantitative-Structure Activity Relationship (QSAR) methodology has been performed to develop models that correlate antimalarial activity of artemisinin analogs and their molecular structures. In this study, the data set consisted of 197 compounds with their activities expressed as log RA (relative activity). These compounds were randomly divided into training set (n=157) and test set (n=40). The initial stage of the study was the generation of a series of descriptors from three-dimensional representations of the compounds in the data set. Several types of descriptors which include topological, connectivity indices, geometrical, physical properties and charge descriptors have been generated. The number of descriptors was then reduced to a set of relevant descriptors by performing a systematic variable selection procedure which includes zero test, pairwise
correlation analysis and genetic algorithm (GA). Several models were developed using different combinations of modelling techniques such as multiple linear regression (MLR) and partial least square (PLS) regression. Statistical significance of the final model was characterized by correlation coefficient, r2 and root-mean-square error calibration, RMSEC. The results obtained were comparable to those from previous study on the same data set with r2 values greater than 0.8. Both internal and external validations were carried out to verify that the models have good stability, robustness and predictive ability. The cross-validated regression coefficient (r2
cv) and prediction regression coefficient (r2 test) for the external test set were consistently greater than 0.7. The QSAR models developed in this study should facilitate the search for new compounds with antimalarial activity.