The growing demand for sustainable solutions to technological and societal challenges has driven significant research efforts to integrate machine learning (ML) techniques into computational physics and chemistry. As ML becomes more prevalent in interdisciplinary research, the amount of comprehensive quantum-mechanical (QM) property data generated in recent years to train robust predictive models has significantly increased. Recently, we introduced high-fidelity property data at the level of non-empirical hybrid density-functional theory (DFT) with a many-body treatment of vdW dispersion interactions (i.e., PBE0+MBD) for both small [Sci. Data 8, 43, (2021)] and large [Sci. Data 11, 742, (2024)] drug-like molecules in equilibrium and non-equilibrium states. These datasets have proven instrumental for advancing QM-based ML interatomic potentials (e.g., SO3LR model [J. Am. Chem. Soc.147, 37 (2025)]) and for improving semi-empirical methods (e.g., EquiDTB model [chemRxiv, 10.26434/chemrxiv-2025-z3mhh]), thereby enabling accurate and efficient molecular simulations. Beyond these advances, the availability of QM structural and property data has also been key to developing novel molecular representations that enhance the accuracy and interpretability of ML models for predicting biological properties—such as toxicity and lipophilicity—of large drug-like molecules [chemRxiv, 10.26434/chemrxiv-2025-hj4dc]. In this presentation, I will discuss our recent developments in these areas.