A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen‑π Interactions by Training Neural Networks

Noncovalent interactions, such as halogen bonds (XB), play a crucial role in molecular recognition and drug design, yet halogen···π contacts remain comparatively underexplored. Here, we report a proof-of-concept QM-AI approach that integrates high-level quantum mechanical (QM) calculations with neural networks (NNs) to predict halogen···π interaction energies. Nearly 1.4 million MP2/TZVPP single-point calculations on halobenzene–benzene complexes were carried out to generate exhaustive training data, which were represented by simple geometric descriptors as input features for machine learning. The resulting neural network model is specifically designed to capture σ-hole-driven halogen···π interactions under well-defined geometric constraints. The resulting model reproduced reference interaction energies with excellent accuracy (R2 = 0.998, RMSE = 0.16 kJ/mol) and maintained strong performance on independent, randomly generated and PDB-derived test sets. Previously, we have demonstrated in a benchmarking study that “gold standard” CCSD(T) energies of this interaction can be appropriately represented by MP2/TZVPP calculations, but at a better calculation efficiency by 2 orders of magnitude (∼102). Consequently, we herein exploit a methodological “extension” from CCSD(T) → MP2 → NNs. Our approach maintains accuracy close to CCSD(T) benchmarks while achieving a runtime acceleration of up to 8 orders of magnitude (∼108) compared to MP2 calculations. This study demonstrates the feasibility of fast, accurate neural network models based on QM data for halogen···π interactions in a QM-AI approach.