To encode the chemistry of input structures (ligands A and B) a per-leg message-passing neural network (MPNN) was used. Input graphs were populated with three inputs, namely atom features (element, #valence electrons, #hydrogen bonds, orbital hybridisation), bond features (bond type, conjugation) and atom pair indices. Whereas the MPNN architecture was based on previous work by Gilmer and DeepChem , the code implementation of this work was primarily based on examples provided by Kensert . Based on information provided during RBFE setup, the atom-mapping (i.e. which R-groups are transformed to which between ligands A and B) is expressed as an array of 50 integers, where each integer index relates to the atom index in ligand A, and the integer value relates to the atom index in ligand B.