---
title: "Gemini-generated In-silico MS/MS Libraries for the Blood Exposome and Metabolomics Workbench"
id: "biorxiv-3-in-silico-tandem-mass-spectral-libraries-for-the-blood-exposome-and"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-3-in-silico-tandem-mass-spectral-libraries-for-the-blood-exposome-and"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1?rss=1"
published_at: "2026-10-25T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Gemini-generated In-silico MS/MS Libraries for the Blood Exposome and Metabolomics Workbench
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-3-in-silico-tandem-mass-spectral-libraries-for-the-blood-exposome-and
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1?rss=1)
- **Published At:** 2026-10-25T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors produced in-silico tandem mass spectral libraries for human-relevant small molecules using the **Gemini** large language model. - Libraries include spectra at high, medium and low fragmentation energy levels; each spectrum was exported in NIST MSP text format for compatibility with metabolomics tools. - Coverage spans 91,231 unique 2D chemical structures drawn from the **Blood Exposome Database** and the **Metabolomics Workbench** repository. - The team used an optimized chain-of-thought prompt and instructions to generate spectra; the source did not detail the exact prompt text or computational parameters. - Technical validation compared Gemini-generated spectra to the **NIST 23** mass spectral database; validation produced a reported 55% accuracy metric. - The manuscript is a preprint and has not undergone peer review; the authors declared no competing interests. - Data and code links are provided via a Zenodo record (reference in the preprint). - Funding sources include NIH/NIEHS and NIH/NCATS grants listed in the preprint, though the preprint does not attribute specific analyses to individual grants. - The authors propose that LLMs can expand in-silico spectral libraries for annotating HRMS data from human biospecimens, while acknowledging that the reported validation accuracy indicates room for improvement.
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Tuan Nguyen Icahn School of Medicine at Mount Sinai * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Tuan%2BNguyen%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Nguyen%20T&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATuan%2BNguyen%2B) Dinesh K Barupal Icahn School of Medicine at Mount Sinai * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dinesh%2BK%2BBarupal%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Barupal%20DK&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADinesh%2BK%2BBarupal%2B) * [ORCID record for Dinesh K Barupal](http://orcid.org/0000-0002-9954-8628 "Open in new tab") * For correspondence: dinesh.barupal@mssm.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5901407/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5901407/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5901407/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5901407/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5901407/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Human body contains endogenous metabolites, nutrients and xenobiotics chemicals. These chemicals can be detected in a biospecimen using high-resolution mass spectrometry instruments, yielding metabolomic and exposomic datasets. Mass spectral libraries are critical components of these workflows to annotate experimental data with chemical information. In this data descriptor, we report mass spectral libraries for human-relevant small molecules, that are generated using Google Gemini large-language model. Using an optimized chain-of-thought prompt and instructions, we have generated mass spectra for each compound at high/medium/low energy fragmentation level. Each spectrum was reported in the NIST MSP text format for easy to use by metabolomics software. These libraries cover 91,231 unique 2D structures from the Blood Exposome Database and the Metabolomics Workbench data repository. Technical validation was shown by comparing the Gemini-generated spectra against NIST 23 mass spectral database, which demonstrated 55% accuracy. LLMs represent a useful and accessible tool for expanding in-silico spectral libraries to annotate compounds in the HRMS data for human biospecimens and may accelerate the compound annotation in metabolomics and exposomics. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * ## Funder Information Declared NIH/NIEHS, U24ES035386, R24ES036917, R01ES035478, R01ES032831, P30ES023515 NIH/NCATS, UL1TR004419 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.20.760278v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/25/2026.10.20.760278.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Data/Code](https://www.biorxiv.org/content/early/2026/10/25/2026.10.20.760278.external-links) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) In-silico Tandem Mass Spectral Libraries for the Blood Exposome and Metabolomics Workbench Databases: Application of Gemini Large Language Model Tuan Nguyen, Dinesh K Barupal bioRxiv 2026.10.20.760278; doi: https://doi.org/10.64898/2026.10.20.760278 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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