1. Training of selected LJ parameters against experimental measurements of physical properties (densities and enthalpies of mixing) using Parsley 1.3.0 valence parameters. 2. Starting from a force field with the refitted LJ parameters (vdw-v1), training valence parameters with QM data with fixed LJ parameters. 3. Validation of the new force field using a variety of quantum mechanical, physical property, and proteinligand binding data. in the first step, selected LJ parameters were refit against liquid densities and enthalpies of mixing sourced from the NIST ThermoML archive, producing an intermediate force field (vdw-v1). In the second step, valence parameters from vdw-v1 were trained against optimized geometries and torsion energy profiles of drug-like molecules, resulting in a new force field (Sage 2.0.0) trained against curated quantum chemical and condensed-phase property data. Both steps optimize force field parameters using a regularized least-squares approach implemented in ForceBalance. Sage 2.0.0 was then benchmarked against test sets of gas phase QM equilibrium structures, solvation free energies, and protein-ligand binding free energies.