Flexibility-Ring Enhanced Graph Neural Network for Property Prediction of Complex Traditional Chinese Medicine Molecular Structures
Property prediction of traditional Chinese medicine (TCM) molecules remains constrained by their complex ring systems and conformational flexibility. Conventional graph neural networks (GNNs) treat molecules as topological graphs, discarding bond length and angle strain information critical for accurate property estimation. This study introduces the flexibility-ring enhanced graph neural network (FRGNN), which augments the graph isomorphism network (GIN) with edge descriptors encoding bond length strain and angle strain, and incorporates multiple favorable conformations to construct multi-graph data. The model was evaluated on two TCM databases comprising 37,822 molecules across seven key molecular properties, benchmarked against three state-of-the-art (SOTA) GNN models and two basic GNN models. FRGNN achieved an average root mean square error (RMSE) reduction of 8.63% relative to the second-best model across all seven tasks. For molecules containing polycyclic and macrocyclic structures, the RMSE reduction reached 10.04%. These results demonstrate that FRGNN outperforms existing SOTA small-molecule property prediction models on TCM compounds, offering a robust computational approach for complex natural product characterization. The incorporation of flexibility and ring-specific descriptors addresses a critical gap in molecular representation learning, enabling more accurate predictions for structurally diverse TCM constituents.