While some target chemical properties can be computed analytically through first-principles methods (e.g., molecular weight) or through empirical, rule-based approaches (e.g., drug-likeness), most desired properties need to be estimated by data-driven QSAR or QSPR models. This can be the case for many reasons: (1) some chemical properties, such as bioactivity, are too complex to be estimated solely from a fixed set of predefined rules; (2) ML predictions are much cheaper to obtain compared to resource-intensive wet-lab measurements or physics-based simulations; or (3) there is a sufficient amount of labelled data that can serve as a starting point for faster convergence to target chemical spaces. We denote properties modeled from data as f θ to distinguish them from properties ϕ that can be computed analytically.