Evidence map›Paper›PMID 40501725›Full record

ArticlebioRxiv : the preprint server for biology2025

Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry.

Runzhong Wang, Mrunali Manjrekar, Babak Mahjour, Julian Avila-Pacheco, Joules Provenzano, Erin Reynolds, Magdalena Lederbauer, Eivgeni Mashin, Samuel Goldman, Mingxun Wang and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Runzhong WangMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Mrunali ManjrekarMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Babak MahjourMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Julian Avila-PachecoBroad Institute of MIT and Harvard, 415 Main St, Cambridge, 02142, MA, United States.
Joules ProvenzanoMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Erin ReynoldsNortheastern University, 360 Huntington Ave, Boston, 02115, MA, United States.
Magdalena LederbauerMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Eivgeni MashinBroad Institute of MIT and Harvard, 415 Main St, Cambridge, 02142, MA, United States.
Samuel GoldmanMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Mingxun WangUniversity of California, Riverside, 900 University Ave, Riverside, 92521, CA, United States.
Jing-Ke WengNortheastern University, 360 Huntington Ave, Boston, 02115, MA, United States.ORCID 0000-0003-3059-0075
Desirée L PlataMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.
Clary B ClishBroad Institute of MIT and Harvard, 415 Main St, Cambridge, 02142, MA, United States.ORCID 0000-0001-8259-9245
Connor W ColeyMassachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States.

Funding

Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Premonopausal Hormone Levels and Risk of Breast CancerR01CA067262 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HANKINSON, SUSAN E · 2002 to 2011
$11.9M
Metabolomic profile of chronic distress in relation to diseases of aging across diverse populationsR01AG051600 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Susan E Hankinson, LAURA D KUBZANSKY · 2017 to 2026
$4.9M
Graduate Training in Computational and Systems BiologyT32GM087237 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BURGE, CHRISTOPHER B · 2009 to 2023
$4.6M
ULTIMATER01AI145781 · NIAID · STICHTING RADBOUD UNIVERSITEIT · PI THWAITES, GUY, VAN CREVEL, REINOUT · 2019 to 2023
$4.4M
NCI NIH HHS R01 CA067262NCI NIH HHS U01 CA176726NIAID NIH HHS R01 AI145781NIA NIH HHS R01 AG051600NIGMS NIH HHS T32 GM087237Wellcome Trust
6 · The paper itself

Abstract

Structural elucidation using untargeted tandem mass spectrometry (MS/MS) has played a critical role in advancing scientific discovery [1, 2]. However, differentiating molecular fragmentation patterns between isobaric structures remains a prominent challenge in metabolomics [3-10], drug discovery [11-13], and reaction screening [14-17], presenting a significant barrier to the cost-effective and rapid identification of unknown molecular structures. Here, we present a geometric deep learning model, ICEBERG, that simulates collision-induced dissociation in mass spectrometry to generate chemically plausible fragments and their relative intensities with awareness of collision energies and polarities. We utilize ICEBERG predictions to facilitate structure elucidation by ranking a set of candidate structures based on the similarity between their predicted

Indexed as

Computational metabolomicsGeometric deep learningStructural elucidationTandem mass spectrometry

Identifiers

PMID40501725
PMCPMC12154671

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.