FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

• URL: http://arxiv.org/abs/2604.16648v1
• Date: Fri, 17 Apr 2026 19:11:18 GMT
• Title: FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
• Abstract summary: We present FRIGID, a framework with a novel diffusion language model that generates structures conditioned on mass spectra.<n>We show how forward fragmentation models enable inference-time scaling by identifying spectrum-inconsistent fragments.<n>Further empirical analyses show that FRIGID exhibits log-linear performance scaling with increasing inference-time compute.
• Score: 52.735012862324766
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via intermediate fingerprint representations and determined chemical formulae, training at the scale of hundreds of millions of unlabeled structures. We then demonstrate how forward fragmentation models enable inference-time scaling by identifying spectrum-inconsistent fragments and refining them through targeted remasking and denoising. While FRIGID already achieves strong performance with its diffusion base, inference-time scaling significantly improves its accuracy, surpassing 18% Top-1 accuracy on the challenging MassSpecGym benchmark and tripling the Top-1 accuracy of the leading methods on NPLIB1. Further empirical analyses show that FRIGID exhibits log-linear performance scaling with increasing inference-time compute, opening a promising new direction for continued improvements in de novo structural elucidation. FRIGID code is publicly available at https://github.com/coleygroup/FRIGID

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