Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection

The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have selected a series of 53 endochin analogs with antimalarial activity against the clinically relevant multidrug resistant malarial strain TM-90-C2B to develop robust QSAR models using different chemometric tools such as stepwise regression, factor analysis followed by multiple linear regression (FA-MLR), factor analysis followed by partial least squares (FA-PLS) analysis, genetic function approximation (GFA) and genetic partial least squares (G/PLS) techniques. We have tried to emphasize on importance of descriptor-thinning prior to feature selection step. For this purpose, we have partitioned the total pool of descriptors into three categories followed by running stepwise regression separately for all three categories of descriptors for selection of variables. Then we have applied genetic methods of model development on the selected pool of descriptors. We have validated the models using different validation parameters to check the statistical quality and reliability of the models. It has been found that models developed from variable selection by stepwise regression followed by GFA and G/PLS are the best two models. This study reflects the importance of descriptor-thinning and noise reduction prior to feature selection for the development of QSAR models. According to the best two models, we interpreted that substitutions with rotatable bond containing groups (e.g., long alkyl chains), limited volume of the molecules, absence of C—R group, presence of electronegative (like —Br) fragments, absence of cycles or rings in the molecules are important for the antimalarial activity. The developed models are believed to comply with the OECD guidelines for QSAR models. The results also confirmed that the QSAR models developed from the reduced pool of descriptors were better than those obtained from the entire pool of descriptors.