Recently, however, the application of machine learning techniques has led to the development of new scoring functions that outperform classical scoring functions in terms of ranking compounds by binding affinity . Like classical scoring functions, these methods use the 3D structure of the protein and an automatically generated binding mode of the ligand to compute structure-based interaction features between the protein and ligand. The binding mode-derived features are then used as inputs to a machine learning algorithm to predict the binding affinity. The features used by such machine learning scoring functions typically focus on capturing interactions between the ligand and the protein, but make limited use of the bulk physical, chemical, and topological properties of the protein and/or the ligand alone. Ligand-based features are widely used to select potential binders in ligand-based virtual screening, and have proven to be very effective in understanding polypharmacological relationships between proteins 24 .