with W o ∈ R h×ho . Here, h o is the size of q, i.e. the sum of the hidden size h and the size of x v . In summary, in Module 2, the D-MPNN weights W i , W h , and W o are learned from the training data, outputting learnable atomic embeddings h v . Customizations and hyperparameter tuning include the choice of activation function τ , the hidden size h, and the number of message passing steps T . Chemprop offers the option to add bias terms to all neural network layers (defaults to False).