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1. Symmetry and ¹H NMR chemical shifts of short hydrogen bonds: impact of electronic and nuclear quantum effects

   https://doi.org/10.1039/C9CP06840F  

   Zhou, Shengmin; Wang, Lu (March 2020, Physical Chemistry Chemical Physics)

   Short hydrogen bonds (SHBs), which have donor and acceptor separations below 2.7 Å, occur extensively in small molecules and proteins. Due to their compact structures, SHBs exhibit prominent covalent characters with elongated Donor-H bonds and highly downfield (>14 ppm) 1H NMR chemical shifts. In this work, we carry out first principles simulations on a set of model molecules to assess how quantum effects determine the symmetry and chemical shift of their SHBs. From simulations that incorporate the quantum mechanical nature of both the electrons and nuclei, we reveal a universal relation between the chemical shift and the position of the proton in a SHB, and unravel the origin of the observed downfield spectral signatures. We further develop a metric that allows one to accurately and efficiently determine the proton position directly from its 1H chemical shift, which will facilitate the experimental examination of SHBs in both small molecules and biological macromolecules. 

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2. Real-time prediction of ¹ H and ¹³ C chemical shifts with DFT accuracy using a 3D graph neural network

   https://doi.org/10.1039/d1sc03343c  

   Guan, Yanfei; Shree Sowndarya, S. V.; Gallegos, Liliana C.; St. John, Peter C.; Paton, Robert S. (September 2021, Chemical Science)

   Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1 H and 13 C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13 C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution. 

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3. LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts

   https://doi.org/10.1021/acs.jctc.5c00026  

   Darrows, Mikayla Y; Kodituwakku, Dimuthu; Xue, Jinze; Pickering, Ignacio; Terrel, Nicholas S; Roitberg, Adrian E (April 2025, Journal of Chemical Theory and Computation)

   This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C′, HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our model is able to predict backbone chemical shifts with root-mean-square errors of 2.53 ppm for N, 0.91 ppm for Cα, 1.14 ppm for Cβ, 1.02 ppm for C′, 0.49 ppm for amide protons, and 0.27 ppm for Hα. The program predicts chemical shifts an order of magnitude faster than the widely utilized SHIFTX2 model. This breakthrough allows us to predict NMR chemical shifts for a very large number of input structures, such as frames from a molecular dynamics (MD) trajectory. In our simulation of the protein BBL from Escherichia coli, we observe that averaging the chemical shift predictions for a set of frames of an MD trajectory substantially improves the agreement with experiment with respect to using a single frame of the dynamics. We also show that LEGOLAS can be successfully applied to the problem of recognizing the native states of a protein among a set of decoys. 

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4. Predicting Solid-state NMR Observables via Machine Learning

   https://doi.org/10.1039/9781837673179-00224  

   Unzueta, Pablo A; Beran, Gregory_J O (March 2025, Royal Society of Chemistry)

   Machine learning is becoming increasingly important in the prediction of nuclear magnetic resonance (NMR) chemical shifts and other observable properties. This chapter provides an introduction to the construction of machine learning (ML) models for predicting NMR properties, including the discussion of feature engineering, common ML model types, Δ-ML and transfer learning, and the curation of training and testing data. Then it discusses a number of recent examples of ML models for predicting chemical shifts and spin–spin coupling constants in organic and inorganic species. These examples highlight how the decisions made in constructing the ML model impact its performance, discuss strategies for achieving more accurate ML models, and present some representative case studies showing how ML is transforming the way NMR crystallography is performed. 

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5. A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

   https://doi.org/10.1039/D0CP03243C  

   Chen, Guang; Shen, Zhiqiang; Li, Ying (September 2020, Physical Chemistry Chemical Physics)

   null (Ed.)

   Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure–activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug–membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights. 

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