Hugging Face Trending Papers
Geometric organization of olfactory descriptor data in the Poincaré disk
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Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors
arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecul...
arXiv Machine Learning
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A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks
The study fine‑tunes the Uni‑Mol2 molecular foundation model on the GS‑LF benchmark for multi‑label odor descriptor prediction. The resulting model matches or surpasses state‑of‑the‑art baselines on the primary benchmark and successfully transfers to four downstream olfactory tasks—including cross‑dataset prediction, odorless vs. odorous classification, enantiomer evaluation, and odor mixture discriminability—without further deep‑learning training. The enantiomer analysis demonstrates that 3D molecular representations can distinguish mirror‑image molecules, a capability lacking in 2D graph models, though predicting stereochemistry’s perceptual effects remains unresolved.
arXiv AI
Jul 8
What Images Cannot Say: Language-Guided Olfactory Representation Learning
arXiv:2607. 06402v1 Announce Type: cross Abstract: Images tell us what a scene looks like, but rarely what it would feel like to be there.
arXiv Machine Learning
Sep 14
Decoding Mixture Perception through Computational Modeling of Component Interactions
The paper introduces a bio‑inspired deep learning framework that models olfactory perception of complex chemical mixtures. It constructs neural response curves for molecule‑receptor interactions, fuses attention‑weighted multi‑receptor and concentration‑dependent multi‑molecule data, and transfers knowledge from molecular associations to improve mixture recognition. The model achieves 92.2% accuracy and offers a generalizable computational pathway from chemical blending to perceptual formation.
arXiv Machine Learning
Sep 10
GraphNOSE: A Graph Transformer in Olfaction
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arXiv Machine Learning
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What an odour descriptor corpus can and cannot measure: valence, attenuation, and the ceiling of the public record
The study evaluates the reliability of shared odor descriptor words across four public corpora from Pyrfume, finding substantial disagreement (I² = 80 %) and limited agreement on descriptor application (median tetrachoric = 0.795, κ = 0.413). Only a fraction of the achievable variance in odor perception is captured by current models and descriptor sets, with valence emerging as the primary missing component. Even with extensive model capacity and merged corpora, the gap remains, indicating that valence must be measured directly to improve machine olfaction.