ArticleJournal of cheminformatics2023
OWSum: algorithmic odor prediction and insight into structure-odor relationships.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Odor-Sight: An Interpretable Graph Neural Network Web Platform for Prediction of Odorant Activity.Journal of chemical information and modeling · 2026Article
- Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.RSC advances · 2026Article
- Construction of a machine learning-based predictive model for odor-driven behavioral preference of odorant molecules.Current research in food science · 2026Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- A comparative study of machine learning models on molecular fingerprints for odor decoding.Communications chemistry · 2025Article
- Revealing the formation of aged aroma in raw Pu-erh tea during the storage through comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and molecular docking.Current research in food science · 2025Article
- An overview on olfaction in the biological, analytical, computational, and machine learning fields.Archiv der Pharmazie · 2025Review
- Odor prediction of whiskies based on their molecular composition.Communications chemistry · 2024Article
- Classification of substances by health hazard using deep neural networks and molecular electron densities.Journal of cheminformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We derived and implemented a linear classification algorithm for the prediction of a molecule's odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.
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