CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions

arXiv:2605.12980v2 Announce Type: replace Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) remains challenging, particularly for de novo generation beyond database coverage. A common approach decomposes the task into spectrum-to-fingerprint prediction followed by fingerprint-to-structure decoding, enabling the use of large-scale molecular corpora. However, at deployment, the decoder relies on predicted rather than oracle fingerprints, introducing structured errors that propagate into generation. Moreover, common autoregressive decoders threshold fingerprint probabilities and serialize active bits as discrete input tokens, blocking molecular generation gradients from the spectrum encoder. We present \textit{CoRe-Gen}, which improves the intermediate condition through synthetic-spectrum pretraining, matches deployment-time noise through frequency-aware corruption, and introduces a differentiable fingerprint-to-memory bridge for joint encoder--decoder finetuning. The bridge preserves soft bit confidences and conditions a structure-aware autoregressive decoder without discrete fingerprint tokenization. CoRe-Gen achieves 21.49\%/35.42\% Top-1/Top-10 exact-match accuracy on NPLIB1 and, under encoder-aligned evaluation, 20.02\%/22.45\% on MassSpecGym, while retaining efficient autoregressive inference.

Abstract:Molecular structure elucidation from tandem mass spectra (MS/MS) remains challenging, particularly for de novo generation beyond database coverage. A common approach decomposes the task into spectrum-to-fingerprint prediction followed by fingerprint-to-structure decoding, enabling the use of large-scale molecular corpora. However, at deployment, the decoder relies on predicted rather than oracle fingerprints, introducing structured errors that propagate into generation. Moreover, common autoregressive decoders threshold fingerprint probabilities and serialize active bits as discrete input tokens, blocking molecular generation gradients from the spectrum encoder. We present \textit{CoRe-Gen}, which improves the intermediate condition through synthetic-spectrum pretraining, matches deployment-time noise through frequency-aware corruption, and introduces a differentiable fingerprint-to-memory bridge for joint encoder--decoder finetuning. The bridge preserves soft bit confidences and conditions a structure-aware autoregressive decoder without discrete fingerprint tokenization. CoRe-Gen achieves 21.49\%/35.42\% Top-1/Top-10 exact-match accuracy on NPLIB1 and, under encoder-aligned evaluation, 20.02\%/22.45\% on MassSpecGym, while retaining efficient autoregressive inference.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2605.12980 [cs.LG]

(or arXiv:2605.12980v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.12980 Focus to learn more arXiv-issued DOI via DataCite

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CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions

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