In the future, we aim to extend Spectro to other spectroscopy techniques, particularly 2D NMR experiments (COSY, HSQC, HMBC) which provide critical information about atomic connectivity through cross-peak correlations. For these 2D NMR data, j-IR-vis's vision-based approach could be adapted to predict cross-peak locations and intensities, enabling the model to capture additional structural features beyond chemical shifts. The modular nature of Spectro's pipeline allows for straightforward integration of new spectroscopic data sources -the embeddings from these additional techniques could be concatenated with the existing IR and 1D NMR embeddings to enhance molecular structure prediction. Additionally, we plan to combine it with multi-task learning approaches for scenarios where specific data are missing, paving the way for more efficient and accurate structure elucidation in various chemical applications.