ArticleChemical senses2021
Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception.
Article in Chemical senses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Combinatorial responsiveness of chemosensory neurons in mouse explants revealed by DynamicNeuroTracker.Cell reports methods · 2025Article
- A Structure-Based Approach for Predicting Odor Similarity of Molecules via Docking Simulations with Human Olfactory Receptors.ACS omega · 2025Article
- Pyrfume: A window to the world's olfactory data.Scientific data · 2024Article
- Regression Study of Odorant Chemical Space, Molecular Structural Diversity, and Natural Language Description.ACS omega · 2024Article
- Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.Molecules (Basel, Switzerland) · 2023Article
- Article
- Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules.Scientific reports · 2022Article
- Article
- A behavioral paradigm for measuring perceptual distances in mice.Cell reports methods · 2022Article
- Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose.The Journal of membrane biology · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework-first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors-to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.
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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.