Chemprop is fully end-to-end trainable, so that the weights for the D-MPNN and FFN are updated simultaneously. Users have the option to train models using cross-validation and ensembles of submodels. By default, a single model is trained on a random data split for 30 epochs. We note that small datasets need a much larger number of epochs to train, and advise to check for convergence of the learning curve. Chemprop uses the Adam optimizer. The default learning rate schedule increases the learning rate linearly from 10 -4 to 10 -3 for the first two warmup epochs and then decreases learning rate exponentially from 10 -3 to