Modeling pyranose ring pucker in carbohydrates using machine learning and semi‐empirical quantum chemical methods

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 (2) of 0.83. This correlation in relative energies lowers to values of 0.70 for ANI‐2x and 0.60 for GFN2‐xTB. The ANI‐1ccx also provides the most accurate estimate of the energetics of the (4)C(1)‐to‐(1)C(4) minimum energy pathway for the six molecules. All three models reproduce chair more accurately than non‐chair geometries. Analysis of small model molecules suggests that the ANI‐1ccx model favors puckers with equatorial hydrogen bonding substituents; that ANI‐2x and GFN2‐xTB models overstabilize conformers with axially oriented groups; and that the endo‐anomeric effect is overestimated by the machine learning models and underestimated via the GFN2‐xTB method. While the pucker conformers considered in this study correspond to a gas phase environment, the accuracy and computational efficiency of the ANI‐1ccx approach in modeling ring pucker in vacuo provides a promising basis for future evaluation and application to condensed phase environments.