The retention time prediction lies in the field of physicochemical properties, thus we also PyTorch Geometric v2.4.0. The GCNN layer takes as input a list of node features, a list of edge features, a connectivity matrix, and returns a list of node embeddings. To obtain the final graph embedding, the node embeddings undergo an aggregation or global pooling step: each feature can be averaged, summed, maximised or minimised across the molecular nodes. We apply both feature averaging and maximisation, so that a list of node embeddings with N features each becomes a graph embedding with 2N features. Each graph embedding is then passed through several dense layers to calculate the final graph property. For each GCNN type, the hyperparameter optimisation included the number of convolutional layers (2-6), dense layers (1-4), and the size of the convolutional layer node embeddings (2 5 , 2 6 , 2 7 , 2 8 ).