Expanding and Enhancing Neural Network-Based QM-AI Models for the Accurate Prediction of Halogen-π Interaction Energies in Protein Contexts

> 0.98 and RMSE < 0.5 kJ/mol) within the targeted σ-hole interaction domain and retain robust performance on independent, randomly generated geometry and PDB-derived test sets. Model limitations are primarily associated with geometric arrangements outside the training distribution, such as π···π, C-H···π, or other non-σ-hole interaction motifs. By augmenting the training data with additional randomly generated geometries, model robustness and generalization were further improved without modifying the underlying network architecture. Overall, this work establishes a scalable and transferable QM-AI strategy for the rapid and accurate prediction of halogen···π interaction energies across diverse aromatic environments, enabling near-quantum-mechanical accuracy at negligible computational cost and supporting future applications in structure-based drug design.