Chemprop supports a multitask constrained D-MPNN architecture for predicting atom-and bond-level properties, such as charge density or bond length. This model enables a D-MPNN to be trained on multiple atomic and bond properties simultaneously, though unlike molecular property targets they do not share a single FFN. Optionally, an attention-based constraining method may be used to enforce that predicted atomic or bond properties sum to a specified molecular net value, such as the overall charge of a molecule. An initial, more limited version of this capability was developed in Ref. 55. For details on the input formats to be used for atom/bond targets, both for training and for inference, see SI.