Figure . Scheme of the generation procedure of Aquamarine (AQM) dataset. We have here investigated molecules containing C, N, O, H, Cl, S, P, and F atoms. Then, we generated the conformers per molecule using the conformational search workflow implemented in CREST code which employs a metadynamics sampling with an additional genetic z-matrix crossing step. A set of representative conformers is selected using an energetic analysis considering the DFTB total energy E tot and many-body dispersion (MBD) energy E MBD , see "Methods". Thus, the current version of AQM dataset contains 59, 783 low-and high-energy conformers of 1, 653 molecules up to 54 non-hydrogen atoms (mean: 28.2) with a total number of atoms ranging from 2 to 92 (mean: 50.9). All structures in the final set of conformers are optimized using DFTB3+MBD and DFTB3+MBD supplemented with the GBSA implicit solvent model of water. Afterward, over 40 molecular (global) and atom-in-a-molecule (local) QM properties of gas-phase and solvated structures are computed by performing single point calculations at the PBE0(tight)+MBD level of theory and further enhanced with the MPB implicit solvent model of water, respectively. Hence, we have two different AQM subsets, namely AQM-gas and AQM-sol. optimized conformers, AQM also includes an extensive number (over 40) of global (molecular) and local (atom-in-a-molecule) QM properties computed at a high level of theory that depends on the chemical environment used during the geometry optimization. The majority of QM properties for gas-phase structures were evaluated using non-empirical hybrid DFT with MBD interactions (i.e., PBE0+MBD) in conjunction with tightly-converged numeric atom-centered orbitals . In addition, MPB implicit solvent model of water was considered to obtain the properties for solvated structures. Hence, we have two different AQM subsets, namely AQM-gas and AQM-sol, which contain the QM structural and property data of molecules in gas phase and implicit water, respectively. Based on its design, AQM holds the potential to enhance the comprehension of the influence of molecule-solvent and collective dispersion interactions in structure-property and property-property relationships of molecules of pharmaceutically relevant size and composition. Key advancements. Our main motivation for proposing AQM as a benchmark dataset is to advance in the development of the next generation of ML models that enable a fast and accurate property estimate and ideation of drug-like molecules synthesized in a chemical environment. In pursuit of this aim, we have ensured that the AQM dataset exhibits the following characteristics,