ArticleThe Journal of organic chemistry2025
Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors.
Article in The Journal of organic chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Molecular Insights into Phage-Hydrogel Polymer Interactions Through Docking, Molecular Dynamics, and Machine Learning.Polymers · 2026Article
- Article
- Quantum Chemistry in Drug Design: Applications, Challenges, and the Need for Speed.Methods in molecular biology (Clifton, N.J.) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study employs machine learning (ML) to assess the predictive power of electronic descriptors derived from natural bond orbital (NBO) analysis for hydrogen bond acceptance. Using a data set of 979 hydrogen bond complexes, each formed by a hydrogen bond acceptor and 4-fluorophenol as the donor, we optimized geometries via GFN2-xTB, followed by DFT single-point calculations. From these, NBO analysis was used to extract intramolecular donor-acceptor interactions, particularly the orbital stabilization energies (
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Registered trials
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