ArticleScience (New York, N.Y.)2023
A principal odor map unifies diverse tasks in olfactory perception.
Article in Science (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- A volatile-based aroma complexity index for Nongxiangxing baijiu.Food chemistry: X · 2026Article
- Molecular Atlas of Key Food Odorants Reveals Mixture-Level Organization and Enables Generative Aroma Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning reveals structural determinants of odor detection thresholds and identifies high-potency food odorants.NPJ science of food · 2026Article
- Decoding Sulfur-Containing Aroma Compounds in Foods: From Key Odorant Mapping to Structure-Odor Mechanisms and Flavor Design.Foods (Basel, Switzerland) · 2026Review
- Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception.bioRxiv : the preprint server for biology · 2026Article
- Rapid odorant metabolism organizes identity- and timing-based odor representations by olfactory bulb inputs and outputs.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence for food innovation.Nature food · 2026Review
- Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Decoding Smell from Receptor Structure.Research square · 2026Article
- Towards a Mathematical Structure of Global Phenomenal Consciousness.Entropy (Basel, Switzerland) · 2026Article
- Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.RSC advances · 2026Article
- Molecular and Computational Basis of Taste Perception: A Review toward the "Digital Language of Taste".ACS omega · 2026Review
- Low rank adaptation of chemical foundation models generates effective odorant representations.bioRxiv : the preprint server for biology · 2026Article
- Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation.Sensors (Basel, Switzerland) · 2026Article
- ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma.Oncology reports · 2026Article
- Overview of AI-Based Scent Creation.Sensors (Basel, Switzerland) · 2026Review
- A comprehensive review of hydrogen sensor for thermal runaway monitoring: fundamentals, recent advancements, and challenges.Microsystems & nanoengineering · 2026Review
- Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents.bioRxiv : the preprint server for biology · 2026Article
- Grounding olfactory perception in language: Benchmarks and models for generating natural language odor descriptions.bioRxiv : the preprint server for biology · 2026Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.
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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.