In terms of formulation property prediction, ML approaches can help identify factors that influence the behaviour of NPs. One commonly explored property is the particle size, which among other factors, influences the pharmacokinetics and biodistribution of the NPs . Additionally, the size affects drug loading capacity and release, in turn influencing the performance of NP-based formulations in vivo . Being able to predict how parameters associated with the method of preparation influence particle size would streamline the formulation process by obviating preliminary optimization studies. An early study that sought to identify influencing factors for particle size of a tri-block copolymer system found that polymer concentration in the organic phase contributes the most to this property. Here, an artificial neural network was trained on a dataset consisting of 43 formulations, prepared manually by nanoprecipitation . While a successful proof-of-concept, such studies are data-limited, which may render the results less transferrable and necessitate strong justification for the choice of complex ML models. Similar challenges in data volume and quality were identified in a recent investigation into ML prediction of drug nanocrystal size. Therein, He et al. collated in-house and literature data (amassing 910 nanocrystal size and 341 PDI datapoints) based on three different methods of preparation (Figure ) . A light gradient boosting machine algorithm performed accurate prediction of size and PDI for two of the methods of preparation, which was further verified experimentally by the preparation of drug nanocrystals for three APIs that were not included in the training dataset used to develop the models. Poor prediction for the third method of preparation (i.e., solvent evaporation) was attributed to the small volume and variable quality of the data . The algorithm calculated the importance of features for the prediction task, which could assist the process of designing new experiments, and helped to contextualize how the ML models performed their predictions . In this way, ML applied to formulation data can extract valuable information that could provide stronger leads for the development process.