Machine learning: NMR Shift and J-Coupling Prediction

尽管人们对机器学习模型应用于辅助药物开发的兴趣有所增加，但很少关注应用这些机器学习技术来改善NMR性质预测。通过开发能够预测这些参数的模型，可以快速加速阐明分子的过程。印象（Intelligent Machine Prediction of Shift and Scalar information of Nuclei）是一个成功的尝试，它可以从3D分子结构中预测NMR参数，与计算密集型量子力学计算的精度相当。通过建立基础IMPRESSION模型，它的功能可以通过以下方式进一步扩展：通过更多样化的训练数据集预测更广泛的分子，提高用于生成训练数据的基础DFT方法的准确性，将参数预测扩展到物理性质，并包括多构象异构体分析。这些发展可能会导致一个非常强大的化学工具包的结构解析。

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.