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DOE OSTI · 2474776

QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules

Burns, Jackson W.·Rogers, David M.
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Abstract

Quantitative structure–odor relationships are critically important for studies related to the function of olfaction. Current literature data sets contain expert-labeled molecules but lack feature data. This paper introduces QuantumScents, a quantum mechanics augmented derivative of the Leffingwell data set. QuantumScents contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with chemprop (Heid, E.; et al. ChemRxiv, 2023, DOI: 10.26434/chemrxiv-2023-3zcfl) to predict scent labels. Finally, the QuantumScents data set is freely available on Zenodo along with the authors’ code, example models, and data set generation workflow (https://zenodo.org/doi/10.5281/zenodo.8239853).

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYcomputer simulationsmolecular modelingmolecular structuremoleculesneurophysiology

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Burns, Jackson W., Rogers, David M.. 2023-11-21. QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules. https://doi.org/10.1021/acs.jcim.3c01338

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