Training with Spectral Data Dropout: The model was trained under conditions of random spectral data omission to enhance resilience and performance in data-constrained environments. Each spectrum had a 50% chance of being omitted and replaced with zeropadded data to maintain input consistency. This stage continued to utilize SMILES and molecular weight losses. Additionally, to quantify each modality's contribution to the predictive accuracy, we performed an ablation study. The fully trained MMT model underwent single epoch fine-tuning iterations, each omitting one spectral modality. Performance comparisons between these ablated models and the complete MMT model elucidated the relative importance of individual spectral data types in molecular structure prediction.