ArticleScientific reports2022
Predicting odor from molecular structure: a multi-label classification approach.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 43 citations in OpenAlex.
- Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.RSC advances · 2026Article
- Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.Communications medicine · 2026Article
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- A comparative study of machine learning models on molecular fingerprints for odor decoding.Communications chemistry · 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
- Design Principles From Natural Olfaction for Electronic Noses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Odor classification: Exploring feature performance and imbalanced data learning techniques.PloS one · 2025Article
- Interpretable multitask deep learning models for odor perception based on molecular structure.Current research in food science · 2025Article
- Odor prediction of whiskies based on their molecular composition.Communications chemistry · 2024Article
- FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data.Journal of cheminformatics · 2024Article
- Predicting odor from vibrational spectra: a data-driven approach.Scientific reports · 2024Article
- Article
- Mlp4green: A Binary Classification Approach Specifically for Green Odor.International journal of molecular sciences · 2024Article
- Volatile Composition of Fortification Grape Spirit and Port Wine: Where Do We Stand?Foods (Basel, Switzerland) · 2023Review
- Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.Molecules (Basel, Switzerland) · 2023Article
- OWSum: algorithmic odor prediction and insight into structure-odor relationships.Journal of cheminformatics · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure-odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.
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