Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.

ABSTRACT: Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict ¹H and ¹³C chemical shifts, CSTShift, that combines atomic features with DFT-calculate

SUBMITTER: Han C 

PROVIDER: S-EPMC11209944 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature