Machine learning of ligand properties requires a consistent, fixed-size vector representation for each ligand. These are typically composed of molecular fingerprints and/or constitutional, topological, geometric, thermodynamic, electronic features of the molecule, an overview of which can be found elsewhere . Here, we explored several representations to encode the ligand library. The first and most complex representation was built from all the features we could compute directly with RDKit 16 from ligand topologies and 3D coordinates. Hence, we refer to this representation as 2D_3D. These features include constitutional and graph descriptors, molecular quantum numbers (MNQs) , pharmacophore feature maps based on lig-ands from both publicly available (4d08 18 , 4d09 18 , 4htx 30 , 6cyd 31 , 6ezf ) and unpublished (shared with us by Janssen Research & Development) PDE2 crystal structures, 2D and 3D autocorrelation and radial density function descriptors , USR and USRCAT shape recognition descriptors, 166-bit MACCS 36 , BCUT2D , MORSE , WHIM , and GET-AWAY fingerprints, as well as molecular surface area descriptors for EState , PEOE partial charges , SMR molar refractivity , and SlogP hydrophobicity .