A DrugEx recurrent-neural net (RNN) model pretrained on Papyrus version 05.5 was used as a baseline model. DrugEx (v3.4.7) was used for model training. Through transfer learning, this model was finetuned to generate molecules close to the known data for the respective data sets. Seven datasets were created for finetuning, one for each objective and one for each combination of the A 2A R and a PK property. For each objective (A 2A R, Cl, VDSS, FU), finetuning was applied to all molecules applicable according to the respective OPS. For combinations of A 2A R and a PK objective, the model was finetuned on all data from the A 2A R dataset that was applicable according to the PK and A 2A R OPS. Each model was finetuned for a maximum of 500 epochs with early stopping based on validation loss of a randomly selected 10% subset of the data. For the reinforcement learning, scores for each objective were normalized to fall between 0-1 using a clipped scoring strategy, where scores below and above a certain threshold are set to 0 and 1 respectively and the values are scaled linearly between the thresholds. See Supplementary Table for details. Thresholds were set to the 10th and 90th percentiles of the property values in the respective dataset. Thirteen scenarios for optimization were configured: maximizing A 2A R, maximizing/minimizing CL, VDSS or FU, maximizing A 2A R, and maximizing/minimizing CL, VDSS or FU. For each of the scenarios, reinforcement learning was run for 2000 epochs, with early stopping with a patience of 300 epochs and a minimum of 200 epochs using a batch size of 512 for 3 replicas.