Molecular Representations. To develop the machine learning models, we selected three molecular fingerprints of different design: (a) Molecular ACCess System (MACCS) Keys (166bit) as a dictionary based fingerprint where each position indicates presence or absence of a predefined structure, (b) Morgan fingerprint with radius 2 (2048-bit) Data Modelability. To a priori estimate the feasibility to obtain predictive binary classification models for each target, we calculated the modelability index (MODI) for each targetassociated dataset. MODI is defined as the proportion of compounds in a dataset for which its nearest neighbor belongs to the same class in a given feature space. For its calculation, we selected as compound representation the three different fingerprints described above and as metric to identify the nearest neighbors the Jaccard distance, defined as: