Atomistic force fields describe the potential energy surface of molecular systems as a function of atomic positions. Force fields, while often relatively simple in functional form, have been widely adopted in computational chemistry and biophysics due to their balance between chemical accuracy and computational efficiency. Molecular dynamics simulations performed with force fields have been used to study the mechanisms of many biological phenomena, including protein folding, membrane transport, identification of active sites, docking of ligands, and protein-ligand binding. Protein-ligand interactions are of particular interest to the pharmaceutical industry, as such methods can accelerate drug discovery by identifying promising candidates in silico. This process, known as computer-aided drug design (CADD), requires quantitatively accurate descriptions of many chemically diverse drug candidates, their interactions with different chemical moieties and accurate predictions of their physical properties in various environments. In order to model the protein-ligand complex, we need accurate force fields for both the specific protein chemistries (many of which have been proposed, validated, and tested ) and the small molecule ligands. As the space of potential drug-like molecules is chemically complex and combinatorially large, a small molecule force field should be able to model a diverse set of molecules with high accuracy. OpenFF Sage 2.0.0 achieves these goals by combining the generality of SMIRNOFF direct chemical perception with extensive parameter refitting to improve accuracy.