Integration of Molecular Weight Loss: This stage refined the model's performance by incorporating molecular weight comparisons, leveraging the MS information. The model generates SMILES strings, from which molecular weights are calculated and compared to the target molecular weights using PyTorch's MSELoss function. This loss was normalized via min-max scaling to align with SMILES loss metrics. The molecular weight loss contribution was gradually integrated into the total loss, increasing by 1% every 50,000 steps. This gradual integration allowed the model to smoothly adapt to the new loss component without disrupting the learning process for SMILES prediction.