Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.
arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.
arXiv:2609.14360v1 Announce Type: new Abstract: In practical molecular characterization, small-molecule structure identification benefits from complementary spectroscopic evidence, but missing, degra...
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
arXiv:2609.14360v2 Announce Type: replace Abstract: Reliable small-molecule identification often requires complementary evidence from multiple spectroscopic measurements. In practice, however, spectr...
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv:2607. 26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs.
The paper introduces MSAlign, a lightweight model that aligns frozen foundation models for mass spectra (DreaMS) and molecules (MolDeBERTa) to improve metabolite identification from MS/MS spectra. It presents a unified framework for representation alignment and contrastive learning, demonstrates that a score fusion strategy further boosts performance at minimal cost, and addresses evaluation challenges by quantifying distribution shift in data splitting strategies. All resources, including datasets, splits, and code, are publicly released to promote reproducible research.
arXiv:2602. 22822v3 Announce Type: replace Abstract: Tandem mass spectrometry (MS/MS) is central to small molecule identification, but current deep learning systems for spectrum prediction still remain difficult to evaluate and deploy in practice.
The paper introduces a semi‑supervised framework that learns to predict nuclear magnetic resonance (NMR) chemical shifts from millions of literature‑extracted spectra without explicit atom‑level assignments. By treating the prediction as a permutation‑invariant set supervision problem, the authors show that optimal bipartite matching can be reduced to a sorting‑based loss, enabling stable large‑scale training. The resulting models outperform state‑of‑the‑art methods, generalize better to diverse molecules, and for the first time capture systematic solvent effects across common NMR solvents.
arXiv:2608. 16082v1 Announce Type: new Abstract: Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous.
arXiv:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.
arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.