# Computational Core

> **NIH NIH U2C** · BATTELLE PACIFIC NORTHWEST LABORATORIES · 2021 · $357,290

## Abstract

COMPUTATIONAL CORE SUMMARY 
The capability to chemically identify thousands of metabolites and other chemicals in clinical samples will revolutionize 
the search for environmental, dietary, and metabolic determinants of disease. Through innovations in computational 
chemistry, we propose to overcome a significant, long-standing obstacle in the field of metabolomics: the absence of 
methods for accurate and comprehensive identification of small molecules without relying on data from analysis of authentic 
chemical standards. A paradigm shift in metabolomics, we will use gas-phase molecular properties, collision cross section, 
MS/MS spectra, accurate mass, and isotopic distribution that can be both accurately predicted computationally and 
consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome. The 
outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) accurately calculating 
chemical properties using an integrated, scalable high-performance computational chemistry pipeline, (ii) generating in 
silico reference data for an initial target of 500,000 molecules comprising both known and novel metabolites, (iii) developing 
and validating a multi-property feature matching approach for unambiguous chemical identification in biomedical samples, 
and (iv) disseminating the computational tools, algorithms, and resources . This work is significant because it enables 
comprehensive and confident chemical measurement of the metabolome. This work is innovative because it utilizes a high- 
throughput, high-accuracy, quantum-chemistry-based computational and chemical informatics platform to predict physical- 
chemical properties of metabolites.

## Key facts

- **NIH application ID:** 10213205
- **Project number:** 5U2CES030170-04
- **Recipient organization:** BATTELLE PACIFIC NORTHWEST LABORATORIES
- **Principal Investigator:** Ryan Scott Renslow
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $357,290
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10213205

## Citation

> US National Institutes of Health, RePORTER application 10213205, Computational Core (5U2CES030170-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10213205. Licensed CC0.

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