ArticleACS omega2025
Deep Learning for Odor Prediction on Aroma-Chemical Blends.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.RSC advances · 2026Article
- Overview of AI-Based Scent Creation.Sensors (Basel, Switzerland) · 2026Review
- The Emerging Role of Olfactory Receptors: From Genomics to Precision Medicine.Molecular diagnosis & therapy · 2026Review
- Stable olfactory receptor activation across odor complexity.iScience · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of deep-learning techniques to aroma chemicals has resulted in models that surpass those of human experts in predicting olfactory qualities. However, public research in this field has been limited to predicting the qualities of individual molecules, whereas in industry, perfumers and food scientists are often more concerned with blends of multiple molecules. In this paper, we apply both established and novel approaches to a data set we compiled, which consists of labeled pairs of molecules. We present graph neural network models that accurately predict the olfactory qualities emerging from blends of aroma chemicals along with an analysis of how variations in model architecture can significantly impact predictive performance.
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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.