This study demonstrates the application of GNN transformers with multi-staged transfer learning to create a predictive model of IRIS spectroscopy using only SMILES codes as input. To create the Graphormer-IRIS model, transfer learning was used to refine the Graphormer-IR model via two separate steps: (i) pretraining on DFTcomputed spectra and (ii) refining on experimental IRIS spectra. DFT-computed spectra for 10,336 ionic molecules were accessed from reference 28, and experimental IRIS spectra were obtained for 312 molecular ions. A key addition to the molecular graph structures that we used as inputs was the introduction of a flexible global node connected to all other (atom) nodes by a special edge type. This global node was used to describe the molecular charge state (i.e., (de)protonated, sodiated). Our best-performing IRIS model achieved a test 𝑆𝐼𝑆 𝜇 = 0.6823 ± 0.0343, which is 9.1𝜎 (t = 15.9) or ~ 44% more accurate than the original Graphormer-IR model (𝑆𝐼𝑆 𝜇 = 0.4749 ± 0.0228; employing no transfer learning) and ~ 21% more accurate than the benchmark quantum-chemical methods (𝑆𝐼𝑆 𝜇 = 0.5616). Moreover, our ML model predictions require seconds on a GPU, rather than hours-to-days for the calculation of a single DFT spectrum on a high performance cluster. Since the IRIS dataset employed in this study is relatively small, transfer learning is necessary to improve model generalizability. Borrowing from the field of NLP, we pretrain models on simplified versions of a task and with synthetic/approximate data, or by analogy, to provide the models with a more knowledgeable starting point. This approach ultimately improves prediction accuracy. Here, we explored using experimental IR spectra for neutral species and DFT-computed harmonic vibrational spectra for molecular ions for pretraining purposes. The optimal scheme was first pretrained on the experimental IR spectra for neutrals, followed by an intermediate refinement step that used DFT spectra, then final refinement and transfer of information to the IRIS spectral predictions. Fine-tuning models on DFT spectra provided Graphormer-IRIS with an improved understanding of charge and additional examples of metabolite IRIS spectra. This approach might also effectively act as an intermediate "cleaning" step, where spectral noise found in the gas/condensed IR spectra are eliminated.