In the light of the upcoming advances for prediction of atomic partial charges with ML methods and their potential use for ML/MM approaches, in this work, we provide a datadriven assessment of the quality of different embedding schemes using a variety of atomic partial charges and atomic polarizabilities. Since one particularly demanding area for multiscale methods is the study of enzymatic catalysis, this work is focused in the solvation of amino acid isomer species, as a valuable stepping stone in understanding solvent effects and molecular interactions in complex biochemical systems. We will denominate all structures in the ANI-1x database that have the same molecular formula as a naturally occurring beta amino-acid "amino acid-compatible", regardless of whether the connectivity of the structure actually corresponds to that of an amino acid. We constructed a data set using all the amino acid-compatible entries, which corresponded to about 80k structures in vacuo. We complemented the previously computed properties with in vacuo extra information (new sets of atomic partial charges, energies, and atom types) and, more importantly, all the amino acid-compatible structures from ANI-1x solvated in explicit water molecules, as well as the coupling energy at the density functional theory (DFT) -electrostatic embedding level and other QM/MM properties. Our data set allowed us to provide an unbiased insight into the performance of different atomic partial charges and embedding schemes to reach the state of the art electrostatic scheme level in multiscale frameworks.