The simplest approach to model construction uses a distance to select amino acid residues proximate to the ligand. Here, residue selection was performed using PyMol with various cutoffs, ranging from d = 2.5 to d = 10.0 Å. Any residue having at least one atom that is within the cutoff distance of any ligand atom is included in the QM model. This method is simple and systematic but its weakness lies in the fact that many biologically important active sites are highly aspherical. A good example is human catechol O-methyltransferase (COMT), which has become something of a computational benchmark system because it has a well-resolved crystal structure, kinetics data, and numerous known inhibitors. It also has a catalytically important Mg 2+ ion, which engenders significant charge transfer and many-body polarization effects that cause especially slow convergence with respect to model size. For examples such as COMT, one might expect a "chemically informed" model-construction algorithm to converge more quickly than a brute-force distance-based approach. Therefore, as alternatives we examine models generated using the Residue Interaction Network Residue Selector (rinrus) toolkit, developed by DeYonker and co-workers. We operate rinrus in one of two modes: "Probe" or "Arpeggio". These are illustrated in Fig. alongside the distance-based method.