Year: 2026
Type: preprint
Abstract: Existing work treats odor detection threshold prediction as a supervised regression problem, assuming published thresholds are clean targets. In current public datasets, they are not: values come from different measurement sources and often disagree for the same molecule. Across the three largest public air-phase compilations, thresholds for identical molecules differ systematically by median factors of 3 to 11. Individual source studies vary around a compound’s underlying threshold by about 1.4 log 10 units, so under an independent-source approximation, a new study is expected to differ from the compiled value by about 1.6 log 10 units. This exceeds the best structure-based model’s error against that value (root-mean-square error 1.34), meaning the model matches the published compilation more closely than a new experiment would be expected to. Although averaging multiple studies makes the compiled value more precise, its remaining uncertainty still accounts for at least one-third of the model’s prediction-error variance, and a single source-study threshold has estimated reliability near 0.5. Changing the molecular representation, by comparison, changes root-mean-square error by only about 0.2 log 10 units. We therefore introduce a joint calibration model that places the compilations on a common scale, estimates source-specific offsets and noise, and improves cross-compilation prediction over a simple fixed-offset correction while providing calibrated uncertainty. These results show that progress in odor detection threshold prediction depends not only on better molecular models, but also on better measurements and methods that account for disagreement across data sources.
Source: ChemRxiv
Authors: Anand R. Mysorekar, Lekha Challappa
Language: English
Subfield: Sensory Systems
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7220866873