Machine Learning has enormous potential in augmenting scientists' capabilities in discovering novel drugs or new materials. To achieve this we need to develop ML models that can accurately predict properties of molecules and their interactions, as well as techniques that enable intelligent searching of discrete and complicated molecular spaces. In addition, I believe it is important for such models to have well calibrated uncertainties as well as providing interpretability to any end-user. I therefore wish to research how this can be done, in particular by exploring how we can build into our models pre-existing scientific knowledge.