A Multimodal Integration Framework for Taste and Odor Prediction and Flavor Molecule Discovery

Independent taste and odor models were trained separately under a common modeling and evaluation protocol, using curated datasets containing 17,202 taste molecules across six categories and 4,983 odor molecules across 138 labels. On held-out test sets, the taste model achieved an accuracy of 95.63% and an AUROC of 97.35%, an AUPRC of 82.05%, and an F1 score of 76.32%, whereas the corresponding values for the odor model were 96.03%, 89.97%, 38.00% and 36.94%.

Branch-specific atom-level attribution analysis provided heuristic structural insights into the Uni-Mol2-derived component of the predictions by associating attribution patterns with specific molecular substructures. The trained models were subsequently used to screen approximately 670,000 natural products and coupled with a ScaffoldCAMD strategy for the constrained design of synthetic flavor candidates under chemical-validity, scaffold-assembly, and physicochemical-property constraints, followed by preliminary structural safety filtering.

Molecular docking was further used to examine plausible receptor–ligand interactions and provide preliminary receptor-level support for selected candidates. This work presents a common multimodal modeling strategy that integrates three-dimensional molecular representations with physicochemical descriptors and is instantiated independently for multilabel taste and odor prediction. Although developed for flavor-related prediction, evaluation on the SIDER dataset provides preliminary cross-task evidence that the multimodal integration strategy can be applied to another imbalanced multilabel molecular property problem; broader transferability to other property domains remains to be established.