MPNNs are powerful models for molecular machine learning tasks but require large training data sets. In contrast, kernel methods with graph kernels are likely more appropriate when training data are limited, as they are easier to train, possess fewer hyper-parameters, and are less susceptible to overfitting . Empirically, graph kernels give performance on par with MPNNs on a variety of molecular prediction tasks .