From Bond Angles to Molecular Curvature: A Geometry-Aware Graph Transformer for Ground-State Conformation Prediction

Accurate prediction of molecular ground-state conformations is essential for understanding structure-property relationships and guiding rational molecular design. Despite progress in deep learning, most current methods lack explicit modeling of key geometric priors, particularly atomic bond angles and their derived higher-order curvature features. This often results in conformations with excessive local strain and suboptimal non-bonded interactions that deviate from the true energy minimum. To address this limitation, we propose GeoGT, a Graph Transformer-based model that integrates two geometric constraints into its core design. The first constraint quantifies local geometric non-ideality using an atomic bond angle variance metric, guiding the model away from high-strain structures. The second is a molecular curvature measure derived from angular defect, which extends Gaussian curvature to discrete molecular graphs to capture global spatial bending and packing, thereby improving long-range non-bonded interactions. GeoGT achieves the best distance-based performance across all evaluated datasets and splits, with particularly pronounced gains on QM9. These results highlight the critical role of explicit geometric regularization in improving high-fidelity ground-state conformation prediction. The code and data are available on https://anonymous.4open.science/r/GeoGT-D826.