An accurate prediction of chemical shifts (δ) to elucidate molecular structures has been a
challenging problem. Recently, noble machine learning architectures achieve accurate …

Recently, machine learning has been successfully applied to the prediction of nuclear
magnetic resonance (NMR) chemical shifts. To build a prediction model, the existing …

Graph neural networks (GNNs) have been proven effective in the fast and accurate
prediction of nuclear magnetic resonance (NMR) chemical shifts of a molecule. Existing …

Prediction of chemical shift in NMR using machine learning methods is typically done with
the maximum amount of data available to achieve the best results. In some cases, such …

Calculation of solution‐state NMR parameters, including chemical shift values and scalar
coupling constants, is often a crucial step for unambiguous structure assignment. Data …

We have developed a deep learning algorithm for chemical shift prediction for atoms in
molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data …

Recently, supervised machine learning has been ascending in providing new predictive
approaches for chemical, biological, and materials sciences applications. In this …

An accurate prediction of NMR chemical shifts at affordable computational cost is very
important for different types of structural assignments in experimental studies. Density …

Accurate calculation of specific spectral properties for NMR is an important step for
molecular structure elucidation. Here we report the development of a novel machine …

Fast and accurate prediction of NMR spectra enables automatic structure validation and
elucidation of molecules on a large scale. In this Article, we propose an improved method of …