To represent the ligand in our models we used a diverse set of molecular descriptors computed using the cheminformatics toolkit RDKit. Using the Descriptors module of the Python RDKit package version 2018.03, we computed a set of 200 molecular descriptors for each ligand. These descriptors are conformation-independent and may be categorized as either (computed) experimental properties (e.g. molar refractivity, logP) or theoretical descriptors derived from a symbolic representation of the molecule. The theoretical descriptors may be further categorized according to the dimensionality of the representation of the molecule from which they are derived. The conformer-independent descriptors we consider are either 1-D compositional properties (e.g. heavy atom counts, bonds counts, and molecular weight) or 2-D topological properties (e.g. fragment counts, topological polar surface area, and connectivity index). Any features with zero variance across the data set, or that were null-valued (i.e. infinite or not computable) within the data set were excluded. We removed the Ipc index (an information theory-derived descriptor) as it produced extreme numerical values for larger molecules (too large to be represented as 32-bit floats). In total, 185 RDKit descriptors were retained. We refer to this set of ligand-based features as "RDKit descriptors" throughout this work.