Nucleus sampling improved upon temperature sampling in terms of lower FCD values (Fig. ), indicating a greater overall similarity of the de novo generated molecules to the pretraining set in terms of structural and bioactivity properties. During transfer learning, nucleus sampling generally improved the quality of the sampled molecules in terms of the novelty of the SMILES strings compared to the best temperature sampling data obtained (Fig. ) . The results were stable over a range of sampling threshold values (Supplementary Table ). However, nucleus sampling did not outperform temperature sampling in terms of the uniqueness, validity, and novelty of the SMILES strings generated after the pretraining (Supplementary Table ). To create a PI3Kγ focused chemical library during transfer learning, we used nucleus sampling with a threshold of 0.85. A total of 5000 SMILES strings were sampled over 50 transfer learning epochs with 10 repetitions (5000 × 50 × 10). A total of 2,500,000 SMILES strings were generated, of which 1,121,735 were valid, unique, and novel compared to both the training and fine-tuning compounds.