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

arXiv: https://arxiv.org/abs/2605.12980 · HTML: https://mlsanity.com/paper/2605.12980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 10 of 10)

- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [MassSpecGym: A benchmark for the discovery and identification of molecules](https://mlsanity.com/paper/2410.23326/) (2024)
- [DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra](https://mlsanity.com/paper/2502.09571/) (2025)
- [Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry](https://mlsanity.com/paper/2407.17492/) (2024)
- [Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks](https://mlsanity.com/paper/1811.08545/) (2018)
- [Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation](https://mlsanity.com/paper/1905.13741/) (2019)
- [Prefix-Tree Decoding for Predicting Mass Spectra from Molecules](https://mlsanity.com/paper/2303.06470/) (2023)
- [MADGEN: Mass-Spec attends to De Novo Molecular generation](https://mlsanity.com/paper/2501.01950/) (2025)
- [MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation](https://mlsanity.com/paper/2510.20615/) (2025)
- [Generative structural elucidation from mass spectra as an iterative optimization problem](https://mlsanity.com/paper/2602.07709/) (2026)

## Abstract (arXiv)

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 textitCoRe-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.