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 neur...

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Päätekijä:	Marc U. Engelhardt (16642483) (author)
Muut tekijät:	Finn Mier (21514038) (author), Markus O. Zimmermann (1284975) (author), Frank M. Boeckler (509961) (author)
Julkaistu:	2025
Aiheet:	Biophysics Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xb ), play simple geometric descriptors maintained strong performance magnitude (∼ 10 level quantum mechanical excellent accuracy (< derived test sets defined geometric constraints better calculation efficiency 4 million mp2 2 </ sup r </ 2 orders tzvpp single study demonstrates specifically designed randomly generated neural networks nearly 1 molecular recognition machine learning integrates high input features herein exploit halogen bonds drug design crucial role capture σ benchmarking study ai approach accurate assessments >< sup 8 orders 16 kj
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A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen‑π Interactions by Training Neural Networks

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