Abstract
Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known 'anchor' molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding 'suspect' molecule. BAM's effectiveness is demonstrated by its success in annotating suspect spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2 % of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.