AIZynthFinder . The network is identical to the architecture of the dense layer following the graph convolutional layers used in the graph recommender neural networks. The input is a 2048-bit ECFP4 fingerprint computed using the Morgan algorithm in RDKit . The input is passed to a fully connected layer with 512 nodes with an ELU activation and a dropout rate of 0.4. Finally, a SoftMax activation yield probabilities of each template in the library. The ECFP recommender model is trained using the exact same train, validation, and test set we used to train the graph convolutional neural networks.