To exemplify the new functionalities described above, we predicted synthetic routes for the antiviral drug Amenamevir. To highlight the versatility of the LinChemIn input interface, we harvested predictions from tree CASP platforms: IBM RXN (model trained with the NextMove Pistachio data-set), AstraZeneca's AiZynthFinder 17 (model trained with the NextMove USPTO data-set)) and Askcos from MIT 37,38 (model trained with the REAXYS data-set). It is worth noticing that, not aiming at a benchmark between platforms and models, we did not attempt to align training sets of prediction options. In the following examples, the canonical SMILES is the molecular hash that defines the identity of Molecules nodes; reagents are omitted from the chemical reaction hash ('r p' : reactants and products), ensuring that only reactant and products contribute to the identity of Chemical Equation nodes.