QSAR-guided generative framework for the discovery of synthetically viable odorants

Abstract The discovery of novel odorant molecules is key for the fragrance and flavor industries, yet efficiently navigating the vast chemical space to identify structures with desirable olfactory properties remains a significant challenge. Generative artificial intelligence offers a promising approach for de novo molecular design but typically requires large sets of molecules to learn from. To address this problem, we present a framework combining a variational autoencoder (VAE) with a quantitative structure-activity relationship (QSAR) model to generate novel odorants from limited training sets of odor molecules. The self-supervised learning capabilities of the VAE allow it to learn SMILES grammar from ChemBL database, while its training objective is augmented with a loss term derived from an external QSAR model to structure the latent representation according to odor probability. While the VAE demonstrated high internal consistency in learning the QSAR supervision signal, validation against an external, unseen ground truth dataset (Unique Good Scents) confirms the model generates syntactically valid structures (100% validity achieved via rejection sampling) and 94.8% unique structures. The latent space is effectively structured by odor likelihood, evidenced by a Frechet ChemNet Distance (FCD) of ≈ 6.96 between generated molecules and known odorants, compared to ≈ 21.6 for the ChemBL baseline. Structural analysis via Bemis-Murcko scaffolds reveals that 74.4% of candidates possess novel core frameworks distinct from the training data, indicating the model performs extensive chemical space exploration beyond simple derivatization of known odorants. Generated candidates display physicochemical properties consistent with ground-truth odorants (mean MW ∼158 Da, LogP ∼1.67) and comparable predicted ADMET profiles. Furthermore, quantum mechanical calculations (GFN2-xTB) verify thermodynamic stability with energy distributions matching known volatiles, and automated retrosynthesis demonstrates practical viability, yielding valid synthesis routes for 100% of candidates, averaging 2.89 steps from commercially available precursors. This integrated approach provides a novel and systematic methodology for applying generative AI to explore chemical space specifically for the discovery of new candidate odorant molecules.