On a wider scope, the coalescence of automation and AI has been formalized in the concept of self-driving laboratories. As the name suggests, these systems accelerate scientific discovery through a self-guided process that employs automation and ML models . Seminal works in this area have focused on the optimization of chemical reactions and design of functional materials . In recent years, self-driving laboratories have helped elucidate information that could have otherwise been overlooked and revealed areas of further investigation for materials discovery . As mentioned previously, the idea of closing the loop is central to self-driving laboratories, which can be achieved computationally through inverse design and generative models. Generative models, which are essentially a means of accomplishing inverse design, learn from input data to generate cases with desirable properties that could have plausibly belonged to the original data. These models have been deployed increasingly in drug design applications, particularly in the generation of new molecules based on the molecular representations of input structures . As such, a natural extension of the generative models and inverse design tools used in materials and drug discovery is their application to related aspects of pharmaceutical formulations (i.e., design of excipients, prediction of formulation properties, etc.). While fully closed-loop self-driving laboratories for the development of pharmaceutical formulations have yet to mature, the groundwork and principles for a data-centric strategy to do so are only gaining further coverage . For instance, Tamasi et al. recently developed an iterative Learn-Design-Build-Test cycle to design copolymers that could enhance the thermal stability of polymer-protein hybrids . Using a combination of active ML (e.g., Bayesian optimization) and automated synthesis, candidate copolymers could be iteratively tested, and the data could be looped back into the workflow to determine important chemical features and identify thermostable polymer-protein hybrids. Likewise, Dalal et al. recently used a Bayesian optimizer to identify and optimize chemical and physical characteristics of polymer formulations for in vitro nucleic acid delivery . Such studies show how ML workflows can perform closed-loop predictions to gain important QSAR insights for advancing formulation development.