Prediction of mass spectra using large chemical language models and verification of adaptability in data-scarce domains

Machine learning methods for predicting the electron ionization mass spectra from molecular structures have shown promise for environmental chemical identification, but their performance under domain-specific data scarcity remains poorly understood. We systematically compare a conventional multilayer perceptron model (NEIMS) with a Transformer-based chemical foundation model (MolFormer-XL) for the electron ionization mass spectrometry spectrum prediction under controlled few-shot conditions. Using fluorine-containing molecules as a broader proxy domain, including a PFAS-like subset, motivated by the practical challenge of detecting novel fluorinated contaminants with limited reference data, we vary the number of domain-specific training examples from 5 to 175 while maintaining fixed validation and test sets. Across all few-shot conditions and three of four evaluation metrics (weighted cosine similarity, intensity-weighted precision, and top-10 precision), MolFormer-XL consistently outperforms NEIMS, while intensity-weighted recall remains comparable between the two models. The largest performance gaps are observed in extreme data-scarcity regimes. These results demonstrate that MolFormer-XL, which combines pre-trained molecular representations with a Transformer-based architecture and learned SMILES embeddings, provides a promising approach for transfer under severe domain-specific data scarcity in environmental mass spectrometry.