(i) Pretraining of Prior Model Compounds were prepared in accordance with the REINVENT pipeline as standardized non-isomeric SMILES. The Prior network was pre-trained on a dataset of 1,442,368 compounds derived from ChEMBL where the molecules were restrained to containing between 10 and 50 heavy atoms and elements {H, B, C, N, O, F, Si, P, S, Cl, Br, I} . Only for public dataset, the compounds included in the ChEMBL dataset were omitted. In the pre-training, the Prior network was trained for a total of 10 epochs with a batch size of 128 with an adaptive learning rate starting from 0.0005. All other settings were set to default . All neural network training was conducted on an NVIDIA GeForce GTX 1650 Ti.