To assess the feasibility of developing binary classification models, we calculated the modelability index (MODI), proposed by Golbraikh et al. MODI measures the proportion of compounds in a data set whose nearest neighbor belongs to the same class within a defined feature space. We calculated the MODI values for the ChEMBL data sets using Molecular ACCess System (MACCS) keys (166-bits) and Morgan Chiral of radius 2 (2048-bits) fingerprints, using the RDKit, NumPy, pandas, and SciPy libraries for Python 3.