Modelling the Quantitative Structure–Activity Relationship of 1,2,4-Triazolo[1,5-a]pyrimidine Analogues in the Inhibition of Plasmodium falciparum

Triazolopyrimidine and its analogues represent important lead structures in anti-malarial research. This study modelled the quantitative structure–activity relationship (QSAR) of 125 congeners of 1,2,4-triazolo[1,5-a]pyrimidine from the ChEMBL database in the inhibition of Plasmodium falciparum using six machine learning algorithms. Recursive feature elimination was used to select the most significant of 306 molecular descriptors with 1 moleculal outlier. A split ratio of 20 % was used to split the x and y matrices into 99 training and 24 test compounds. The regression models were built using machine learning scikit-learn algorithms (multiple linear regression (MLR), k-nearest neighbours (kNN), support vector regressor (SVR), random forest regressor (RFR), RIDGE regression and LASSO). Model performance was evaluated using the coefficient of determination (R²⁾, mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) p-values, F-statistic and variance inflation factor (VIF). The number of significant variables considered to build the model was 5 (p < 0.05) with the regression equation pIC₅₀ = 5.90–0.71npr1–1.52pmi3+0.88slogP–0.57vsurf-CW2+1.11vsurf-W2. On 5-fold cross validation, three algorithms, KNN (MSE=0.46, R²=0.54, MAE=0.54, RMSE=0.68), SVR (MSE=0.33, R²=0.67, MAE=0.46, RMSE=0.57) and RFR (MSE=0.43, R²=0.58, MAE=0.51, RMSE=0.66) showed robustness, high efficiency, and reliability in predicting the pIC₅₀ of 1,2,4-triazolo[1,5-a]pyrimidine. The models provided useful data on the functionalities necessary for developing more potent 1,2,4-triazolo[1,5-a]pyrimidine analogues as anti-malarial agents.