# Predicting Human Olfactory Perception from Molecular Structure

> **NIH NIH R01** · MONELL CHEMICAL SENSES CENTER · 2021 · $437,424

## Abstract

PROJECT SUMMARY
Modern technology makes it possible to capture a visual scene as a photograph, alter it, send it to another
country nearly instantaneously, and store it without concern for degradation. None of this is currently possible
in olfaction. Although perfumers and flavorists are adept at mixing odorous molecules to produce a desired
perceptual effect, the rules underlying this process are poorly understood at a quantitative level. Current
methods for displaying odors to a subject are akin to requiring a Polaroid of every visual stimulus of interest. A
more efficient method for probing the olfactory system would be to use a set of 'primary odors'—some limited
number of odors from which all other complex odors could be reproduced by appropriate mixtures. Both
auditory and visual stimuli have been digitized, and this will eventually be possible in olfaction as well.
Predicting odor from chemical structure has been a problem in the field since its inception, but recent advances
in machine learning algorithms have made great progress in analogous problems, such as facial recognition.
The research proposed here will combine these machine learning techniques with high quality human
psychophysics to understand how to predict the smell of a molecule or mixture of odorants, which will
ultimately help improve our understanding of disease diagnosis using odors as well as eating-related health
and illness.

## Key facts

- **NIH application ID:** 10249061
- **Project number:** 5R01DC017757-02
- **Recipient organization:** MONELL CHEMICAL SENSES CENTER
- **Principal Investigator:** Joel D Mainland
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $437,424
- **Award type:** 5
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249061

## Citation

> US National Institutes of Health, RePORTER application 10249061, Predicting Human Olfactory Perception from Molecular Structure (5R01DC017757-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10249061. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*