The outcomes of external validation mirrored those of the internal cross-evaluation, affirming the CATB algorithm as a robust predictive model, as evidenced by its R 2 , RMSE, and MAE values of 0.859, 0.505, and 0.270 respectively. A comparative analysis of the five top-performing algorithms from internal crossvalidation, including KNN, RF, XGB and Histogram gradient boosting (HIST), 38 demonstrated CATB's superior generalization capabilities, unequivocally conforming its superior performance, detailed in Table . Consequently, the CATB algorithm was selected for constructing QSAR model.