Five hyperparameters of the model were optimized. Two hyperparameters are related to input vector generation: the diameter and the number of bits of ECFP. Three hyperparameters are related to the random forest model: the number of trees, the number of data points in each leaf node, and the ratio of tested features to find the best split. Except for the number of data points per leaf nodes, increasing the size of the model did not improve the performances of models (Table and Table ). After extensive hyperparameter optimization, ECFP4 with 4096 bits, 1000 trees, 1/3 feature ratio, and 1000 data points per leaf