In the second sub-experiment, PromptSMILES is trained to both optimize scaffold decoration to increase the predicted probability of D 2 activity, as well as, adhere to a selective reaction filter. The selective reaction filter specifies that the first attachment point should be decorated via an amide coupling and the second via a Buchwald-Hartwig reaction. Otherwise the same parameters apply as in the first part of the experiment. Table shows that for 4/8 configurations, more successful compounds are found than LibINVENT resulting in a higher yield, and with a higher average score (examples shown in B12 and optimisation curves in B13). Meanwhile, the ratio of satisfied reaction filters doesn't quite outperform LibINVENT for any configuration. However, considering that in comparison there is no pre-training based on molecules already sliced by these reaction rules as in LibINVENT, PromptSMILES manages to first adapt to the task of scaffold decoration then learn to decorate the scaffold such that in the best case approximately 80% of molecules satisfy both reaction filters. Similar to the first sub-experiment, this improves for a selected configuration to approximately 83%, if the first epochs are allocated to transfer learning.