Integration of Machine Learning in the Characterization of Hydrogen Bonding Analysis: A Comparative Study: Integration of Machine Learning in the Characterization of Hydrogen Bonding Analysis: A Comparative Study
2025
Samuel, H.S. | Etim, Emmanuel E. | Nweke-Maraizu, Ugo | Yakubu, Shedrach
The area of computational chemistry and molecular modelling has undergone a revolution as a result of machine learning's emergence as a potent and adaptable tool for studying hydrogen bonding interactions. An overview of machine learning's uses and developments in hydrogen bonding characterisation is given in this abstract. Machine learning algorithms have been successfully used to predict hydrogen bond properties, identify donors and acceptors, and analyze complex molecular structures with high accuracy and efficiency. These algorithms include supervised and unsupervised learning methods, and deep learning architectures. Molecular interactions are better understood by combining machine learning with other computational techniques including quantum mechanical calculations, molecular dynamics simulations, and docking investigations. This has improved hydrogen bonding analysis. Additionally, explainable AI methods have improved the interpretability of models, enabling researchers to comprehend the variables driving model predictions. Our understanding of chemical and biological processes has substantially improved because to the widespread use of machine learning in hydrogen bonding analyses, which has also given us important new knowledge about molecular behaviour, drug design, materials research, and other fields of study. Future developments in algorithms and data representation are anticipated to further enhance the model's precision, understand ability, and scalability, fostering new ideas and scientific understanding of hydrogen bonding characterisation.
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