ArticleJournal of chemical information and modeling2026
Expanding and Enhancing Neural Network-Based QM-AI Models for the Accurate Prediction of Halogen-π Interaction Energies in Protein Contexts.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In this study, we extend a previously introduced QM-AI strategy for predicting halogen···π interaction energies from a single aromatic model (representing phenylalanine) to multiple biologically relevant aromatic environments. Herein, neural network models were developed for halogen···π interactions involving phenol, imidazole, and indole, serving as model systems for the aromatic side chain residues of tyrosine, histidine, and tryptophan. Large, systematically generated datasets of halobenzene-aromatic system complexes (in total, over 18 million interaction geometries) were evaluated at the MP2/TZVPP level of theory and represented by compact geometric descriptors to train residue-specific neural networks. Across all systems, the models reproduce quantum-mechanical reference energies with high accuracy (R
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