Comparison of QM Methods for the Evaluation of Halogen−π Interactions for Large-Scale Data Generation

Halogen−π interactions play a pivotal role in molecular recognition processes, drug design, and therapeutic strategies, providing unique opportunities for enhancing and fine-tuning the binding affinity and specificity of pharmaceutical agents. The present study systematically benchmarks various combinations of quantum mechanical (QM) methods and basis sets to characterize halogen−π interactions in model systems. We evaluate both density functional theory (DFT) methods and wave function-based post-HF methods in terms of accuracy to reference calculations at the CCSD­(T)/CBS level of theory and runtime efficiency. By balancing these crucial aspects, we aim to identify an optimal configuration suitable for high-throughput applications. Our results indicate that MP2 using the reasonably large TZVPP basis set is in excellent agreement with reference calculations, striking a balance between accuracy and computational efficiency. This allows us to generate large, reliable data sets, which will serve as a basis to develop and train machine-learning models capable of accurately capturing the strength of halogen−π interactions, thereby providing a robust data-driven foundation for medicinal chemistry analysis.