FFNNs take a molecular fingerprint, here a Morgan fingerprint as implemented in RDKit, as input and transform it into the respective target property. We used FFNNs as implemented in Ref. 56, where we omitted the message-passing. Model training used a hidden size of 300, two feed-forward layers, and 200 epochs of training unless indicated otherwise.