DualLGD reformulates molecular graph denoising as alternating atom and bond subproblems in separate streams, achieving 34.37% and 23.89% top-1 accuracy on NPLIB1 and MassSpecGym benchmarks, roughly 3x prior state of the art.
Ms-bart: Unified modeling of mass spectra and molecules for structure elucidation
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 4years
2026 4roles
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CoRe-Gen reaches new state-of-the-art exact-match accuracy on the NPLIB1 benchmark for de novo molecular structure generation from mass spectra by using synthetic pretraining, frequency-aware corruption, and structure-aware decoding to close the gap between clean training data and noisy deployment.
FRIGID scales a diffusion-based model for de novo molecular structure generation from mass spectra, reaching over 18% top-1 accuracy on MassSpecGym and tripling prior bests on NPLIB1 via large unlabeled training and inference-time fragmentation refinement with log-linear compute scaling.
MSAlign aligns frozen DreaMS and ChemBERTa models with MLPs and candidate-based contrastive learning to outperform prior methods on molecule retrieval from MS/MS spectra while quantifying distribution shift in data splits.
citing papers explorer
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Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
DualLGD reformulates molecular graph denoising as alternating atom and bond subproblems in separate streams, achieving 34.37% and 23.89% top-1 accuracy on NPLIB1 and MassSpecGym benchmarks, roughly 3x prior state of the art.
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CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions
CoRe-Gen reaches new state-of-the-art exact-match accuracy on the NPLIB1 benchmark for de novo molecular structure generation from mass spectra by using synthetic pretraining, frequency-aware corruption, and structure-aware decoding to close the gap between clean training data and noisy deployment.
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FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
FRIGID scales a diffusion-based model for de novo molecular structure generation from mass spectra, reaching over 18% top-1 accuracy on MassSpecGym and tripling prior bests on NPLIB1 via large unlabeled training and inference-time fragmentation refinement with log-linear compute scaling.
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MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification
MSAlign aligns frozen DreaMS and ChemBERTa models with MLPs and candidate-based contrastive learning to outperform prior methods on molecule retrieval from MS/MS spectra while quantifying distribution shift in data splits.