QuantumStinks: Quantum-Mechanical Properties for 3.5k Olfactory Molecules

Quantitative Structure-Odor Relationships are critically important for studies related to the function of olfaction. Current literature datasets contain expert-labeled molecules but lack feature data. This paper introduces QuantumStinks, a quantum-mechanics augmented derivative of the Leffingwell dataset. QuantumStinks 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 to predict scent labels. The QuantumStinks dataset is freely available on Zenodo along with the authors' code, example models, and dataset generation workflow.