To address the challenge of the vast chemical space that no model can be trained on entirely, we integrated an improvement cycle that activates when the model encounters an unfamiliar region not covered in the training data. This cycle employs a generative model designed to suggest structurally similar molecules within the unexplored chemical space, allowing for the creation of a fine-tuned dataset tailored to these novel regions. Coupled with this process is a data generation pipeline, which includes the SGNN network for generating 1 H and C NMR spectra and rule-based algorithms for reconstructing HSQC and COSY spectra, as well as for calculating coupling constants in 1 H NMR spectra. Furthermore, IR spectra are generated using a message-passing neural network. For mass spectrometry (MS) data, we calculate the exact molecular weight from the SMILES representation of each molecule using RDKit, simulating the molecular ion peak that would be observed in highresolution MS. This comprehensive approach ensures that all relevant spectral modalities, including MS data, are represented in the fine-tuning dataset, enhancing the model's ability to adapt to new chemical spaces.