Flexibility-Ring Enhanced Graph Neural Network for Property Prediction of Complex Traditional Chinese Medicine Molecular Structures

• • FRGNN reduced average RMSE by 8.63% across seven molecular property prediction tasks compared to the second-best SOTA model, directly improving predictive reliability for TCM compound screening where experimental characterization is resource-intensive. • • For molecules containing polycyclic and macrocyclic structures, FRGNN achieved a further 10.04% RMSE reduction, addressing the historically poor performance of topological GNNs on complex ring systems that dominate natural product scaffolds. • • The model was trained and validated on 37,822 molecules from two TCM databases, providing a statistically robust benchmark that covers the structural diversity of natural products, unlike conventional datasets biased toward synthetic small molecules. • • Integration of bond length strain and angle strain edge descriptors, combined with multiple favorable conformations, enabled the GIN backbone to capture geometric strain effects that correlate with thermodynamic and biological properties, reducing prediction error where topology-only models fail.