SMILES structures were converted into molecules to calculate 16 types of fingerprints or descriptors, called molecular features, including 3D-Mordred, RDKit descriptor, Mol2vec, MACCS, PubChem, Avalon, ECFP2, ECFP4, ECFP6, RDK5, RDK6, RDK7, Cats2D, 2D-Pharmacophore Gobbi (Ph4), MAP4, and SECFP (raw_data_features). The data preparation process was performed for all molecular features set, including target normalization with the threshold of pIC50 being 7 (meaning active or class 1 if pIC50 is equal or above 7, and inactive or class 0 for the counterpart), dataset division (80:20) with stratification principle resulted in 1995 compounds in the training set and 499 compounds in the external validation set with the imbalance ratio of 0.404 between the active and inactive classes.