ArticleJournal of chemical information and modeling2025
A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen-π Interactions by Training Neural Networks.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Expanding and Enhancing Neural Network-Based QM-AI Models for the Accurate Prediction of Halogen-π Interaction Energies in Protein Contexts.Journal of chemical information and modeling · 2026Article
- Key contemporary considerations for halogens in drug discovery.Future medicinal chemistry · 2026Article
- Prevalence of Halogens in 2025 FDA-Approved Drugs: Highlight on Suzetrigine, the First Nonopioid Painkiller in Decades.Journal of medicinal chemistry · 2026Article
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Authors and funding
4 authors.
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Abstract
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 (
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