The dataset underwent a 9:1 train-test split, with the training data further divided 9:1 to create a validation set. Training was executed in multiple five-epoch stages using four Nvidia V100 GPUs, with a batch size of 256 molecules (64 per GPU), spanning approximately seven days per stage. The PyTorch Lightning framework was utilized for efficient multi-GPU training management. The AdamW optimizer was employed with an initial learning rate of 1e -4 . Learning rate adjustment was managed by the ReduceLROnPlateau scheduler, which reduced the rate by a factor of 0.5 following two consecutive epochs without loss improvement. For the improvement cycle fine-tuning, the learning rate was adjusted to 2e -4 when using the simulated PubChem dataset. When working with the ACD Labs generated data and the experimental dataset, the learning rate was further increased to 3e -4 , a 50% spectral dropout was applied, and the molecular weight loss contribution was set to 100%. These modifications in learning rate, dropout, and loss contribution facilitated more effective fine-tuning on specific datasets.