Machine learning: NMR Shift and J-Coupling Prediction

Though there has been an increase in interest in the application of machine learning models into assisting drug development, little focus has been placed into applying these machine learning techniques to improve NMR property prediction. By developing a model that is capable of predicting these parameters, the process of elucidating molecules can be rapidly accelerated. IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information of Nuclei) was a successful attempt at predicting NMR parameters from a 3D molecular structure comparable to the accuracy of computationally intensive quantum mechanical calculations.

By building upon the foundational IMPRESSION model, the capabilities of it could be further expanded by: predicting a wider range of molecules through a more diverse training dataset, increasing the accuracy of the underlying DFT method used to generate the training data, expanding the parameter prediction into physical properties, and including multi-conformer analysis. These developments could result in a highly robust chemical toolkit for structure elucidation.