Author

Satoki Muto

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Aug 2026

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.

Satoki Muto, Akiko Kumada, Masahiro Sato · Applied Physics Letters · 0 citations