GeoRC: Geometry-Aware Residual Correction for Three-Dimensional Molecular Graph Prediction

Three-dimensional graph neural networks predict molecular and atomistic properties from interatomic geometry. Existing geometric backbones are compared mainly by replacing one architecture with another, so any accuracy gain is attributed to the new design as a whole. GeoRC augments an existing graph-level predictor with a geometry-aware residual branch while preserving its base prediction path. Radial distance features and element-wise edge gating build directed local interactions, gated recurrent unit updates refine the node states, and graph-level residual fusion combines the resulting candidate correction with the base prediction through a bounded, magnitude-conditioned screening map and a learnable global scale. The complete GeoRC-SphereNet model lowers MD17 force mean absolute error (MAE) for all eight evaluated molecules by 3.14% on average. In the repeated component-deletion study, removing the radial basis features increases mean force MAE for all eight molecules, which identifies the geometric encoding as the component with the broadest effect, and the OC20 macro energy-MAE reduction is 0.0639% across three paired runs. The results support GeoRC as an effective residual design for molecular and atomistic property prediction, and the proposed method provides a useful reference for future work in this area.