arXiv Machine Learning June 30, 2026 By Rui-Xi Wang, Runzhong Wang, Connor W. Coley

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

Read the original on arXiv Machine Learning →

arXiv:2606. 29161v1 Announce Type: new Abstract: Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 11

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

arXiv:2606. 11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds.

By Emily Nguyen, Yongchan Hong, Harsh Toshniwal, Yan Liu, Andreas Luttens

llms rag multimodal benchmarks safety

arXiv AI

Jun 15

FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction

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.

By Yunhua Zhong, Yixuan Tang, Yifan Li, Pan Liu, Zhiwen Yang, Jie Yang, Jun Xia

benchmarks

arXiv Machine Learning

Jun 5

End-to-End Subgraph Detection with GraphDETR

arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.

By Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

llms computer-vision

arXiv Machine Learning

Jul 28

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

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.

By Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jun 9

Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy

arXiv:2606. 09541v1 Announce Type: cross Abstract: Single-Molecule Force Spectroscopy (SMFS) provides unprecedented insights into biomolecular mechanics, yet the high-throughput generation of force-extension trajectories creates a severe data curation bottleneck.

By Jorge Rodriguez-Ramos

efficiency

arXiv Machine Learning

Jul 1

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

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.

By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik

llms benchmarks

Related stories

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Prediction

End-to-End Subgraph Detection with GraphDETR

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table