C NMR data is provided as 1D input, using only chemical shifts without intensity information. IR spectra (400-4000 cm -1 ) are discretized and down sampled to a total of 1000 datapoints. These datapoints are then embedded to match the 128-dimension input expected by the model. After embedding of each spectrum, a rectified linear unit (ReLU) activation is applied, and a mask is used to identify actual data points. Finally molecular weight and formula embeddings are concatenated to each spectrum embedding to be considered in each encoder type. Encoder: Individual spectrum encoders, each comprising 6 encoder blocks with 16 attention heads and a 4x forward expansion, process the embeddings. The resulting feature vectors are then concatenated and fed into a cross-modality encoder with an identical structure. This cross-modality encoder employs self-attention mechanisms to mix and correlate information from different spectral inputs, allowing the model to identify inter-spectral relationships. Decoder: A 6-block and 16-attention head decoder processes the encoder output along with target SMILES strings. HSQC & COSY Matching: The HungDist-NN algorithm is used for HSQC & COSY peak matching. It compares the HSQC & COSY spectra of generated analogs against the target molecules' spectra. In this process, each generated molecule's spectrum is compared to the input spectrum, and molecules are ranked according to their individual HSQC and COSY errors. These rankings are then combined to produce a final overall ranking. The analog with the lowest spectral discrepancy, as determined by the best combined ranking, is identified as the most likely correct structure.