Thiazolyl–pyrimidine hybrid plays significant roles in the biological activities and SAR of thiazolylpyrimidines (Tzpd), thiazolopyrimidines, and thienopyrimidines due to 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
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Thiazolyl–pyrimidine hybrid plays significant roles in the biological activities and SAR of thiazolylpyrimidines (Tzpd), thiazolopyrimidines, and thienopyrimidines due to 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: multiple linear regression (MLR), k-Nearest Neighbors (kNN), Support Vector Regressor (SVR), and Random Forest Regressor (RFR) to predict the pIC
50 of the test set. The models were evaluated using R
2, 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. Fivefold cross-validation performance scores of MLR, kNN, SVR, and RFR showed that the performance metrics of MLR (MSE = 0.1453; R
2 = 0.680; MAE = 0.290; RMSE = 0.381; pIC
50(predicted) = 8.06 − 0.45vsurf_G + 0.37FCASA- − 0.42MNDO_LUMO − 0.20E_str + 0.30vsurf_HB1 − 0.38vsurf_DD12) outperformed other models. The study developed predictive models and provided insights into the chemical features necessary for the optimization of thiazolyl–pyrimidine to enhance antiplasmodial activity.
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