Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
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: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:2609.05694v1 Announce Type: new Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...
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:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
AromaGen is a 12‑channel wearable system that translates free‑form natural‑language descriptions into olfactory experiences by selecting from 12 base odorants that span a semantically derived olfactory space. The system supports iterative refinement via natural‑language feedback. In a study with 60 participants and 50 benchmark smells, users could identify AromaGen‑generated scents above chance, and AI‑generated compositions performed comparably to human experts, with feedback improving perceived similarity.