Chemprop and PyTorch Geometric GCNNs are neural networks, and each of them is typically trained for an established number of epochs. In particular, Chemprop suggests using 20 epochs. However, it is more convenient to split the training set into two parts (e.g., as 9:1) to track each epoch performance and select the model before overfitting. It is essential to establish such splitting in the training set and not involve the external set to obtain reliable performance estimation.