Unlike other datasets in the field of digital olfaction, the MORE-Q datasets contain an extensive set of quantum-mechanical (QM) properties for the building blocks of graphene-based molecular sensors, as well as binding features among the sensor components (see Fig. ). Indeed, structural properties were obtained using the semi-empirical GFN2-xTB method with D4 dispersion correction , which is known to generate correct geometries at an efficient computational cost. While the energetic, atomic forces and other property calculations were performed at the more accurate level of theory such as PBE with D3 dispersion correction , as implemented in the VASP code. By doing this, we have guaranteed both sufficiently accurate QM properties and feasible computational time consumption.