Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool. Accuracy loss and slow speed affect the identification of compounds through matching of mass spectra using a large-scale spectral library. Here the authors use Word2vec spectral embedding and hierarchical navigable small-world graph to improve accuracy and speed of spectral matching on their own million-scale in-silico library.

Automated summary

This study proposes a fast and accurate spectrum matching method called FastEI, which improves the accuracy and speed of matching by using Word2vec spectral embedding and hierarchical navigable small-world graph. The study shows that FastEI achieves a speedup of two orders of magnitude compared with the commonly used weighted cosine similarity method and achieves high accuracy when identifying compounds using a million-scale in-silico library.