Here, the finetuned model was used as the agent (updated during training). A grid search was performed to find optimal values for epsilon (rate of mutation) and which network to use as the mutation network (fixed during training). The results of the grid search can be found in Supplementary Table . The fine-tuned model was selected as the mutation network and a value of 0.1 was selected for epsilon. The stopping criteria was the improvement in the arithmetic mean of the objectives scores. To determine the reward in the reinforcement learning framework ranking by Pareto front and sub-ranking with Tanimoto distance was used. After optimization for each scenario and replicate, 10000 molecules were generated, which were evaluated on validity, uniqueness, novelty, and chemical diversity.