MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model

2026-07-26

AI Summary

This work addresses the challenging task of library-free de novo molecular structure elucidation directly from tandem mass spectrometry (MS/MS) data by proposing a posterior-querying framework based on a conditional molecular language model. The approach formulates structure generation as a spectrum-induced posterior sampling process, mitigating the train-inference mismatch through a restructured fingerprint decoding paradigm that incorporates active bit density calibration and consensus ranking by generation frequency. Lightweight domain adaptation is achieved via LoRA adapters, while molecular fingerprint and formula conditioning, together with band-limited posterior sampling, further enhance performance. The method achieves state-of-the-art results with Top-1 accuracy of 29.8% and 23.9% on NPLIB1 and MassSpecGym, respectively, and Top-10 accuracy of 41.1% and 28.7%.

Abstract

Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry. Most existing approaches to MS/MS identification remain tied to reference libraries or predefined candidate sets, whereas de novo methods aim to generate structures directly from spectra. A common de novo route predicts a molecular fingerprint from the spectrum and then decodes structures from it, enabling decoder pretraining on large molecule-only corpora. However, this paradigm creates a training-inference mismatch: the decoder is trained on oracle fingerprints computed from molecules, but at inference it is queried with a noisy spectrum-induced fingerprint posterior that is typically collapsed to a single thresholded fingerprint. We introduce MS-GPT, which recasts fingerprint-mediated de novo elucidation as spectrum-induced posterior querying of a conditional molecule-language model. MS-GPT conditions a molecule-language model on fingerprints and formulas, then converts the spectrum-induced posterior into a band of fingerprint queries near the oracle-fingerprint manifold through active-bit density calibration. Candidates sampled across this band are pooled and ranked by generation-frequency consensus. A lightweight LoRA adapter further mitigates domain-specific posterior bias while preserving the pretrained molecular prior. On NPLIB1 and MassSpecGym, MS-GPT sets a new state of the art, reaching Top-1/Top-10 exact-match accuracy of 29.8\%/41.1\% and 23.9\%/28.7\%, respectively. Candidate-pool scaling shows that efficient autoregressive molecular generation continues to improve recall with a little additional inference cost. The source code and model checkpoints are available at https://github.com/VIKI623/MS-GPT.

Problem

• MS/MS
• de novo structure elucidation
• molecular fingerprint
• spectrum-induced posterior
• molecule-language model

Innovation

• de novo structure elucidation
• molecule-language model
• spectrum-induced posterior
• active-bit density calibration
• LoRA adapter