PharmaGNN
: a model for odor prediction based on graph neural networks
Yunshu Liu, Qiumeng Song, Muhammad Shoaib, Xiaofei Nan, Ji Ma, Kai Cui, Yuan Shang, Jinshuai Song Abstract
BACKGROUND
Artificial intelligence (AI)‐assisted odor prediction holds significant potential for applications in fragrance discovery, food quality control, and sensory evaluation. However, the accuracy of existing models is often limited by insufficient features and class imbalance across diverse odor labels. We propose the PharmaGNN model that integrates pharmacophore features to capture essential molecular structural characteristics for odor prediction. An asymmetric loss function is employed during model training to mitigate label imbalance.
RESULTS
Evaluations demonstrate that PharmaGNN achieves state of‐the‐art performance with the highest AUROC score among comparative models. Even when evaluated across 232 labels, the model maintains robust generalization performance. Ablation studies show that both the pharmacophore features and the molecular graph information are significant to the accuracy.
CONCLUSION
PharmaGNN serves as a practical tool to accelerate the design of novel flavor molecules and improve quality control and odor assessment in the fragrance and food industries. This model bridges the gap between cheminformatics and structural biology, offering a robust tool for the rational design of functional scents and the digitization of olfaction. © 2026 Society of Chemical Industry.