Future work could consider multi-target learning with low and high fidelity datasets for multiple molecular properties, e.g., combining activity coefficients, solvation free energies, and partition coefficients. For this, also thermodynamics relationships between the properties could be integrated into the model training and architecture, as, e.g., in , aiming at more general predictive models.