This study utilizes simulated spectroscopic data across multiple modalities: 1 H NMR, C NMR, COSY, HSQC, IR spectra and mass spectrometry (MS) information. The 1 H and C chemical shifts are generated using the Scalable Graph Neural Network (SGNN). These shifts serve as the basis for both 1D NMR information and the generation of 2D spectra. Two-dimensional NMR spectra (COSY and HSQC) were reconstructed from the SGNN-predicted 1 H and C shifts. For HSQC, we employed a reconstruction logic validated against state-of-the-art simulation software. A similar rule-based approach was implemented for COSY spectra, accounting for molecular structure and H-H coupling patterns. To simulate 1 H peak splitting patterns, we developed a rule-based algorithm considering Jcouplings, producing spectra that mimic real-world 1 H NMR data. C shifts were presented without intensity values, consistent with experimental practices. IR spectra were simulated using ChemProp-IR, a directed message passing neural network. MS information, specifically molecular weights and formulas was derived from the SMILES structures using RDKit, simulating the output of highresolution LCMS measurements. While exact LCMS data was not directly processed in our workflow, our approach assumes accurate molecular weight and formula determination from MS analysis. For data handling, spectral data for each modality (except IR) is stored in CSV files containing SMILES strings, unique sample identifiers, and corresponding spectral information. IR spectral data is stored individually, with file names serving as molecular identifiers. In preprocessing, chemical shift data for 1 H and C NMR spectra are normalized by factors of 10 and 200, respectively, with this normalization also applied to the relevant dimensions in the HSQC and COSY spectra. IR spectra are down sampled to 1,000 data points along the frequency dimension. Prior to training, all data is consolidated into a single .pkl file, with spectra, SMILES strings, and sample identifiers stored in a dictionary format, ensuring efficient data retrieval during the training process. Detailed information on the reconstruction logic, simulation of coupling constants and splitting patterns, and limitations of the spectral simulations can be found in Supplementary Information Section 1.