ArticleJournal of computational chemistry2022
Modeling pyranose ring pucker in carbohydrates using machine learning and semi-empirical quantum chemical methods.
Article in Journal of computational chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and machine learning potential.Chemical science · 2026Article
- Capturing Carbohydrate Conformations and Hydration Interactions with a Polarizable Bond Dipole Potential.Molecules (Basel, Switzerland) · 2026Article
- Conformational Free Energy Landscape of β‑Glucose in the Gas Phase and Aqueous Solution: Energetic, Structural, and Electronic Changes.ACS omega · 2025Article
- Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials.Chemical science · 2024Article
- Reinvestigation of the internal glycan rearrangement of Lewis a and blood group type H1 epitopes.Physical chemistry chemical physics : PCCP · 2024Article
- Chitosan Polysaccharides from a Polarizable Multiscale Approach.ACS omega · 2023Article
- Modeling pyranose ring pucker in carbohydrates using machine learning and semi-empirical quantum chemical methods.Journal of computational chemistry · 2022Article
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Authors and funding
2 authors.
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Abstract
Pyranose ring pucker is a key coordinate governing the structure, interactions and reactivity of carbohydrates. We assess the ability of the machine learning potentials, ANI-1ccx and ANI-2x, and the GFN2-xTB semiempirical quantum chemical method, to model ring pucker conformers of five monosaccharides and oxane in the gas phase. Relative to coupled-cluster quantum mechanical calculations, we find that ANI-1ccx most accurately reproduces the ring pucker energy landscape for these molecules, with a correlation coefficient r
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