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A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl-pyrimidine hybrid derivatives against Plasmodium falciparum
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1  Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria Nsukka, 410001 Enugu, Nigeria
Academic Editor: Julio A. Seijas


Thiazolyl-pyrimidine hybrid plays significant roles in the biological activities and SAR of thiazolylpyrimidines (Tzpd), thiazolopyrimidines and thienopyrimidines due the combination of the thiazole and pyrimidine pharmacophores. The study developed regression-based models for the prediction of antiplasmodial activity of 43 Tzpd hybrid obtained from the ChEMBL database. The molecular descriptors (145 features) were scaled down to 6 using the recursive feature elimination. The X- and Y-matrix were split into 34 train and 9 test sets using a split ratio of 0.20. Regression models were built using scikit-learn algorithms: k-Nearest Neighbours (kNN), Support Vector Regressor (SVR) and Random Forest Regressor (RFR)) to predict the pIC50 of the test set. The models were evaluated using R2, mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), p-values, F-statistic, and variance inflation factor (VIF). Of the 145 features calculated for the 43 TzPd, 6 molecular features: FCASA-, MNDO_LUMO, E_str, vsurf_HB1, vsurf_G and vsurf_DD12 (p < 0.05; VIF < 5) were found to significantly influence the antiplasmodial activity. Five-fold cross-validation performance scores of kNN, SVR, and RFR showed that the performance metrics of SVR (MSE = 0.052; R2 = 0.610; MAE = 0.174; RMSE = 0.230; pIC50(predicted) = 8.06 - 0.45vsurf_G + 0.37FCASA‒ - 0.42MNDO_LUMO – 0.20E_str + 0.30vsurf_HB1 – 0.38vsurf_DD12) outperformed models. The study developed predictive models and provided insights into the chemical features necessary for the optimization of thiazolyl-pyrimidine to enhance antiplasmodial activity.

Keywords: Machine learning, Plasmodium falciparum, QSAR, Regression, Thiazolylpyrimidines