MassSpecGym: A benchmark for the discovery and identification of molecules

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F24%3A00605077" target="_blank" >RIV/61388963:_____/24:00605077 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/24:00380612

Result on the web

<a href="https://doi.org/10.48550/arXiv.2410.23326" target="_blank" >https://doi.org/10.48550/arXiv.2410.23326</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.2410.23326" target="_blank" >10.48550/arXiv.2410.23326</a>

Result language

angličtina

Original language name

MassSpecGym: A benchmark for the discovery and identification of molecules

Original language description

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

NeurIPS 2024. 38th Conference on Neural Information Processing Systems

ISBN

979-8-3313-1438-5

ISSN

—

e-ISSN

—

Number of pages

18

Pages from-to

—

Publisher name

NeurIPS

Place of publication

—

Event location

Vancouver

Event date

Dec 10, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001633227600182