Masahiro Sato

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

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.