To address this challenge, Goldman et al. [2] have recently introduced the Metabolite Inference with Spectrum Transformers (MIST) tool to annotate MS/MS spectra. MIST incorporates a novel "chemical formula transformer" aimed at integrating domain-specific knowledge into the architecture of deep neural networks. This approach deviates from traditional methods that represent peaks in MS/MS spectra as discrete, binned values to process them with neural networks. Instead, MIST represents spectra through the chemical formulas associated with their peaks, which are then processed by a transformer neural network. MIST further enriches its model by encoding neutral loss relationships between fragment ions and incorporating substructure prediction as an auxiliary training objective. To address the challenge of limited large-scale training datasets in the domain of small molecule MS, MIST employs a strategy to simulate spectra, thereby expanding the size and diversity of its training corpus. Additionally, rather than directly predicting full-length fingerprints, MIST adopts a progressive inference methodology by first predicting lower-resolution fingerprints, which are subsequently refined into their full-resolution counterparts.