To further explore the potential of graph machine learning for these tasks, we leverage the representation power of ring-enhanced graph neural networks, O-GNN. With O-GNN, we observed a substantial improvement in prediction accuracy on tasks ESOL, BCF and Clint, relative to the best-performing feature-based models. This improvement may be attributed to the increased capacity of the O-GNN architecture to capture information related to the molecular structures relative to the molecular descriptors or fingerprints employed in the baseline models. On the O 3 -react and SO 4 -react tasks, the performance of O-GNN was found to be comparable to best-performing feature-based models, without the substantial gains in RM SE test observed on the other tasks. One plausible explanation is that, the datasets for tasks O 3 -react and SO 4 -react contained fewer observations than those for the other tasks.