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arXiv · 2603.13342

MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search

Meng Tsai·Alexzander Dwyer·Estelle Nuckels·Yingfeng Wang
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Abstract

Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite-spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search-based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite-spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite-spectrum matches as negative samples for training. Experimental results show that our tool, MS2MetGAN, achieves better overall performance than existing metabolite identification methods.

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Meng Tsai, Alexzander Dwyer, Estelle Nuckels, Yingfeng Wang. 2026-03-07. MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search. https://arxiv.org/abs/2603.13342

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