Other methodologies have been proposed like those by Pesek et al. that integrate data from IR, H and 13 C NMR, and mass spectra to build molecular structures, simulating the process a spectroscopist would follow. More recently, some models have been developed to process IR or H and C NMR spectra, transforming spectral data into tokenized text formats to predict molecular structures as SMILES. Other frameworks assess structural connectivity by processing 1 H and C NMR spectra, predicting substructures and assembling candidate isomers with probabilistic rankings. Additionally, DeepSAT, a CNN-based system, uses HSQC spectrum data for scaffold prediction. The NMR-TS method combines machine learning and density functional theory to automate molecule identification from NMR spectra. However, this neural network generates candidate structures without directly considering the spectra, relying on chance to predict the correct molecules. In IR spectroscopy, advancements in deep and convolutional neural networks now enable functional group identification from FTIR spectra without relying on databases or rule-based methods. However, these current approaches still face limitations, such as 1) applicability domain & reliance on extensive databases 2) need for suggestion of target molecule 3) limited consideration of multiple data modalities Our approach addresses these limitations by proposing an automated pipeline from spectra to molecular structure. This pipeline leverages the Transformer neural network architecture that can simultaneously process multiple spectroscopic data types ( 1 H-NMR, C-NMR, HSQC, COSY, IR, and MS). The Transformer's attention mechanism allows it to focus on the most relevant spectral features across different data types, enabling it to learn complex relationships between spectral inputs and molecular structures.