With recent advances in graph machine learning, direct graph representation of molecular structures, where nodes represent atoms and edges represent chemical bonds, has become a viable alternative to chemical descriptors. Following this approach, Duvenaud et al. created data-driven features, NeuralFPs, by applying convolution operations directly to molecular graphs and showed the resulting representation to be better performing than ECFP features. Subsequent work has solidified these results, with graph neural networks achieving state-of-the-art accuracy for a variety of molecular machine learning tasks. Recently, ring-enhanced graph neural network (O-GNN ) has been reported as an advancement to existing graph-machine-learning methods, by explicitly encoding information on rings into graph neural networks. It shows state-of-the-art accuracy on molecular property prediction benchmarks.