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Aroma — evaluated and deferred

Flavor = taste + aroma. The taste side ships and is clean (sweet/bitter/umami ~0.95 AUROC + intensity + sour/salty). The aroma side is deliberately deferred — not for lack of effort, but because the public, commercially-clean data is insufficient. This documents the evaluation, so the decision is legible.

What an aroma model needs

A model mapping a molecule's structure → odor descriptors (sweet, floral, woody…). The state of the art is the Principal Odor Map (Lee et al., Science 2023; Google/Osmo), reimplemented open-source as OpenPOM (BioMachineLearning/ openpom, MIT) — a message-passing GNN trained on ~5,000 expert-labeled molecules (the GS-LF dataset).

The licensing wall (audited)

OpenPOM's code is MIT, but its training data is restricted. A full audit of the Pyrfume catalogue (see DATA-SOURCES.md) found that every rich odor-descriptor dataset is proprietary or NonCommercial — Leffingwell, GoodScents, Arctander, Flavornet (©Datu), FlavorDB / FooDB (NC), OlfactionBase ("all rights reserved"), AromaDB (CSIR), Dravnieks (ASTM ©), sharma_2021 (ACS ©), snitz_2019 (CC-BY-NC). The only commercially-clean odor-descriptor set is keller_2016 (Keller & Vosshall 2016, BMC Neuroscience, CC-BY-4.0; ~480 molecules, 20 descriptors).

The empirical result

We aggregated keller_2016 (training/build_aroma_dataset.py) and trained one RandomForest regressor per descriptor on 2048-bit Morgan fingerprints (training/train_aroma.py), scored by honest 5-fold cross-validation (400 trees):

descriptor CV-R² descriptor CV-R² descriptor CV-R² descriptor CV-R²
acid −0.24 cold −0.19 fruit −0.05 sour −0.05
ammonia −0.15 decayed −0.20 garlic −0.12 spices −0.12
bakery −0.22 edible −0.19 grass −0.45 sweaty +0.03
burnt −0.13 fish −0.04 musky −0.23 sweet −0.05
chemical −0.10 flower −0.12 warm −0.28 wood −0.08

All 20 descriptors scored CV-R² ≤ 0 (range −0.45 to +0.03) — every model is worse than predicting the mean. 0/20 usable heads.

Root cause

keller_2016 is naive-subject data: random volunteers rating unfamiliar molecules on a 0–100 scale. People can't reliably name what a molecule smells like, so the labels are noise (the per-descriptor means compress to ~22–30 for nearly every molecule). The learnable odor data uses expert labels — which is exactly the data that's restricted. That is the structural reason the entire field trains on GS-LF, and why a clean public aroma model isn't currently possible.

Decision

We do not ship a negative-R² model — it would output confident, wrong smells, and a flavor chemist would catch it instantly (worse than nothing). predict_aroma() returns an honest "not available" marker, and we lead with the taste engine.

The OpenPOM scaffold (training/train_odor.py) is kept as the aroma engine for when clean fuel exists:

  1. License PMP 2001 (~$2,775, Leffingwell & Associates) → re-curate → train.
  2. A customer's own odor data (the paid pilot) → train on-prem.
  3. A future open expert-labeled dataset, if one emerges.

Until then: aroma comes with your data — which is on-thesis (public data proves the method; the customer's data unlocks the rest).