The fine-tuning dataset was split into ten train and validation splits (80% to 20%) to find the optimal number of fine-tuning epochs. Early stopping on validation loss was used, with patience of five epochs and tolerance of 10 -5 . The experiments suggested 45 epochs to be optimal and then the pre-trained model was fine-tuned on the whole dataset accordingly. The models of the last five fine-tuning epochs were used to design molecules. 10,240 designs for temperature values ranging from 1.0 to 2.0 (step size 0.25) were generated per temperature and model, totaling 5 × 5 × 10, 240 = 256K designs. The novel and unique molecules among those designs were ranked by their fine-tuning log-likelihood (equation ( )) and the top 5000 molecules were selected for further analysis.