A DrugEx pre-trained RNN generator was fine-tuned on different datasets of applicable molecules for the different optimization tasks (Figure ). After fine-tuning, the models were optimized for the respective single (maximize/minimize A 2A R/FU/VDSS/CL) or combined tasks (maximize A 2A R + maximize/minimize FU/VDSS/CL) (Figure ). Subsequently, each fine-tuned and reinforced model was used to generate 10000 compounds. Validity, uniqueness, novelty (presence in the dataset), applicability, synthetic accessibility, and chemical similarity of the generated compounds were determined and compared (Table ). For the fine-tuned generators a slight drop in the fraction of valid (i.e. parsable by RDKit) molecules (0.75-0.87) compared to the pre-trained generator (0.96) was found. This corresponds with the observed initial drop and gradual recovery of validity during fine-tuning (Supplementary Figure ). However, the fraction of valid compounds of the pre-trained model is restored after reinforcement learning (0.91-0.99). This suggests increasing the patience in fine-tuning in future research may be beneficial for the validity of generated molecules. The uniqueness also decreases after fine-tuning, which reflects the smaller area of chemical space the model covers.