{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-principal-odor-map-unifies-diverse-tasks-in","title":"A principal odor map unifies diverse tasks in olfactory perception","arxiv_id":null,"date":"2023-09-01","proceeding":"Science 2023 9","authors":["Brian K. Lee","Emily J. Mayhew","Benjamin Sanchez-Lengeling","Jennifer N. Wei","Wesley W. Qian","Kelsie A. Little","Matthew Andres","Britney B. Nguyen","Theresa Moloy","Jacob Yasonik","Jane K. Parker","Richard C. Gerkin","Joel D. Mainland","Alexander B. Wiltschko"],"abstract":"Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.","url_abs":"https://www.science.org/doi/10.1126/science.ade4401","url_pdf":"https://www.science.org/doi/10.1126/science.ade4401","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-principal-odor-map-unifies-diverse-tasks-in","repo_url":"https://github.com/BioMachineLearning/openpom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"odor-descriptor-prediction","task_name":"Odor Descriptor Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"mpnn","method_name":"MPNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/odor-descriptor-prediction-on-multi-labelled","task":"Odor Descriptor Prediction","dataset":"Multi-Labelled SMILES Odors dataset","model":"MPNNPOMModel (OpenPOM)","rank_in_archive_order":1,"of":1,"metrics":{"Avg. ROC-AUC":"0.8872"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}