Prediction of mass spectra using large chemical language models and verification of adaptability in data-scarce domains
TL;DR
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.
Abstract
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.