We hypothesized that the O-GNN architecture may require model training on a larger dataset to achieve optimal predictive performance than the feature-based model architectures. Here and going forward, we will compare the best-performing graph machine learning methods, i.e. O-GNN, and the best-performing feature-based methods. We denoted them as O-GNN and 'feature-based' models, respectively, since they are the most desirable options for the two categories of experimented molecular machine learning methods.To test our hypothesis on the data size, we conducted a data-efficiency experiment, in which a series of models were trained on randomly sampled subsets of the BCF and Clint datasets utilizing both O-GNN and a feature-based architecture. The performance of each model was evaluated against varying training set size, by 5-fold cross-validation, to give learning curves (Figure ). features that make up a molecule into a continuous latent representation. In Figure