FlavorGPN: a graph neural network for multi-label molecular flavor prediction

Predicting chemosensory attributes from chemical structures is a fundamental task in cheminformatics and molecular modeling. When applied to flavor specifically, this task becomes particularly challenging due to the structural diversity of flavor molecules and the complex, multi-label nature of human sensory perception. Traditional machine learning methods often rely on one-dimensional fingerprints, which inadequately capture high-dimensional topological and geometric features. In this study, we introduce the FlavorGraph Predictive Network (FlavorGPN), a novel graph neural network (GNN) framework for multi-label flavor prediction. FlavorGPN leverages the pretrained 2D graph encoder from GraphMVP, whose parameters are learned through 3D-informed pretraining, to enhance molecular graph representations. Notably, no explicit 3D conformers or atomic coordinates are used during downstream fine-tuning or inference. Therefore, the use of 3D information in this study should be understood as 3D-supervised pretraining rather than direct 2D/3D geometric integration during inference. To mitigate the class imbalance inherent in flavor datasets, we propose ML-ROS-improved, an adaptive oversampling algorithm that integrates dynamic thresholding for minority-label identification, weighted minority-label sampling, and constrained graph augmentation. We also systematically evaluate several graph augmentation strategies. Among them, Molecular Connectivity Index (MCI)-constrained augmentation achieves the highest observed Macro-F1 and Macro AUC-ROC scores. Across the FlavorMiner and FART benchmarks, FlavorGPN achieved the highest observed Macro-F1 and Macro AUC-ROC point estimates among the evaluated baselines under the reported experimental settings. On the FART benchmark, the model achieved a Macro-F1 score of 0.8542 and a Macro AUC-ROC score of 0.9796. Literature-based contextual comparisons further indicate that the unified model performs competitively on key flavor categories, including Sweet, Bitter, and Sour. However, these comparisons do not constitute controlled head-to-head evaluations. Overall, the benchmark results demonstrate the practical value of FlavorGPN for imbalanced multi-label chemosensory prediction under the evaluated settings and suggest its potential to support computational screening and molecular-level analyses of flavor-associated chemical properties.