1. graph representation learning , such as message passing neural networks (MPNNs) that learn task-specific vector representations of molecular graphs for prediction tasks in an end-to-end manner 2. graph kernels , which (loosely speaking) measure the similarity between any two input graphs, allowing for the use of kernel methods , such as support vector machines , kernel regression/classification , and Gaussian processes , for prediction tasks.