Then, the data mining process was conducted on the training set and applied similar methods for the external validation set. First, 1995 compounds with molecular features underwent data removal to eliminate duplicate rows and columns, followed by missing values handling utilizing KNNImputer from the Scikit-learn library (only for the 3D-Mordred dataset) before ending up with low variance removal using a threshold being 0.05. Next, Local Outlier Factor (LOF) was employed with a parameter setting of "n_neighbors = 20" to remove outliers in the training set and novelty in the external validation set. The final step in the data mining process is data normalization using a rescaling method, in which MinMaxScaler was applied to map all data into the range [0,1].