arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.
arXiv:2606. 19624v1 Announce Type: new Abstract: Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery.
arXiv:2604. 16648v2 Announce Type: replace Abstract: Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.
arXiv:2607. 04774v1 Announce Type: new Abstract: Untargeted tandem mass spectrometry (MS/MS) detects thousands of small molecules per biological sample, yet most go unidentified because they are absent from spectral libraries.
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.
GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily on reference spectral library matching, limiting t...
The paper introduces DiffGCMS, a discrete graph diffusion model that generates molecular structures directly from GC–EI–MS spectra, and couples it with a large language model for post‑processing. In the first stage, DiffGCMS produces candidate structures; the LLM then validates, repairs, and reranks these candidates while offering interpretable explanations of fragment‑ion peaks. On a large NIST 20 test set, the combined approach improves accuracy and ensures 100% candidate validity for small molecules, demonstrating the value of spectrum‑aware post‑processing.
MolP2SBench is a new benchmark that tests whether large language models can reason from molecular properties to structure. Each task presents a masked scaffold and a set of physical properties, asking the model to pick the correct structure from plausible candidates. By shuffling or replacing properties, the benchmark evaluates whether models truly rely on correct property‑structure pairings and how they respond to counterfactual changes, using a Property‑Grounded Recovery metric to compare performance across models.
The study introduces Malaria-Instruct, a curated instruction-following dataset for malaria virtual screening, and evaluates five open-source large language models (Gemma-2, TxGemma, and LlaSMol-Mistral) against classical machine learning baselines and proprietary models. Fine‑tuned LLMs outperform all baselines, with TxGemma-9B achieving the highest ROC‑AUC (0.731 ± 0.005) and LlaSMol-Mistral-7B delivering the best enrichment factor (EF@1% ≈ 4.99). The results demonstrate that domain‑specific fine‑tuning and chemistry‑aware pretraining are essential for reliable discrimination, positioning fine‑tuned open‑source LLMs as a resource‑efficient alternative for antimalarial virtual screening.
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:2606. 05225v1 Announce Type: cross Abstract: Untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) detects thousands of molecular features per sample, yet only 2-20% receive confident structural annotations.
arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.