A significant challenge in simulating the physicochemical properties of large drug-like molecules lies in the fact that, in experiments, their conformations and electronic structures are influenced by interactions with the surrounding solvent. However, the standard approach in contemporary QM simulations involves running them in gas phase, without accounting for moleculesolvent interactions. Unlike another recently published dataset of large molecules (see Table ), AQM dataset considers the molecule-solvent interactions as well as a treatment of van der Waals (vdW) interactions in its generation proceduretwo important physical and chemical effects in determining structural conformations and stability rankings of molecules of pharmaceutically relevant size. As mentioned above, the AQM comprises the structural and electronic data of 59, 783 gas-phase and solvated (low-and high-energy) conformers of 1, 653 molecules with up to 54 non-hydrogen atoms (N ≤ 92), including C, N, O, F, P, S and Cl. The structures of solvated conformers were obtained using DFTB3+MBD method supplemented with the GBSA implicit model of water. This model has been successfully used in the study of free solvation energies of neutral/ionic molecules and the folding of short peptides . Whereas, the level of theory selected to compute the QM properties per conformer was PBE0+MBD supplemented with the MPB implicit model of water. The MPB model has been shown to provide a more accurate description in the study of diverse electrochemical reactions . In all calculations, many-body dispersion (MBD) interactions have been included to deal with long-range interactions that are not adequately represented by the baseline level of theory. These advanced theoretical models have thus generated a more accurate collection of molecular and atom-in-a-molecule (as well as ground state and response) QM properties of conformers in implicit water, which are stored in AQM-sol. Moreover, when integrated with the property information in AQM-gas, these data can assist in fine-tuning ML models for the precise estimation of electronic properties of solvated molecules, e.g., via a delta learning approach.