ArticlePLoS computational biology2013
Predicting odor perceptual similarity from odor structure.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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.
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.
Who cites it
51 citing papers in PubMed.
- Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception.bioRxiv : the preprint server for biology · 2026Article
- Decoding Smell from Receptor Structure.Research square · 2026Article
- Overview of AI-Based Scent Creation.Sensors (Basel, Switzerland) · 2026Review
- Behavioral Relevance of Early Neural Coding of Low-Level Odor Features in Humans.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A Structure-Based Approach for Predicting Odor Similarity of Molecules via Docking Simulations with Human Olfactory Receptors.ACS omega · 2025Article
- Deep Learning for Odor Prediction on Aroma-Chemical Blends.ACS omega · 2025Article
- Odors as cognitive constructs: history of odor classification and attempts to map odor percepts to physical and chemical parameters.Chemical senses · 2025Review
- DeepNose: An Equivariant Convolutional Neural Network Predictive Of Human Olfactory Percepts.ArXiv · 2024Article
- Pyrfume: A window to the world's olfactory data.Scientific data · 2024Article
- Decomposition of an odorant in olfactory perception and neural representation.Nature human behaviour · 2024Article
- Nature and human well-being: The olfactory pathway.Science advances · 2024Review
- What Does the Human Olfactory System Do, and How Does It Do It?Annual review of psychology · 2024Review
- A principal odor map unifies diverse tasks in olfactory perception.Science (New York, N.Y.) · 2023Article
- High-precision mapping reveals the structure of odor coding in the human brain.Nature neuroscience · 2023Article
- Physicochemical features partially explain olfactory crossmodal correspondences.Scientific reports · 2023Article
- OWSum: algorithmic odor prediction and insight into structure-odor relationships.Journal of cheminformatics · 2023Article
- Olfactory abstraction: a communicative and metacognitive account.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Perceptual metrics for odorants: Learning from non-expert similarity feedback using machine learning.PloS one · 2023Article
- A behavioral paradigm for measuring perceptual distances in mice.Cell reports methods · 2022Article
- Hyperbolic odorant mixtures as a basis for more efficient signaling between flowering plants and bees.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.