Molecular Melodies: Unraveling the Hidden Harmonies of NMR Spectroscopy.

This work explores the evolution of auditory analysis in NMR spectroscopy, tracing its journey from a supplementary tool to visual methods such as oscilloscopes, to a technique sidelined due to technological advancements. Despite its renaissance in the late 1990s with artistic and scientific applications, widespread adoption was hindered by the necessity for hardware modifications and reliance on specialized software. Addressing these barriers, this paper introduces a new feature in Mnova NMR software that facilitates the easy auditory interpretation of NMR signals. We discuss new applications of this tool, emphasizing its utility in aiding the identification of specific functional groups by auditory analysis of the spectrum's multiplets, such as distinguishing between aromatic, olefinic, or aliphatic protons, thereby enriching the interpretative capabilities of NMR data.

Direct deduction of chemical class from NMR spectra.

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. The method identified as suitable for classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found to be suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to spectral interpretation problems.

Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR.

Fragment-based drug discovery (FBDD) and validation of small molecule binders using NMR spectroscopy is an established and widely used method in the early stages of drug discovery. Starting from a library of small compounds, ligand- or protein-observed NMR methods are employed to detect binders, typically weak, that become the starting points for structure-activity relationships (SAR) by NMR. Unlike the more frequently used ligand-observed 1D NMR techniques, protein-observed 2D 1H-15N or 1H-13C heteronuclear correlation (HSQC or HMQC) methods offer insights that include the mechanism of ligand engagement on the target and direct binding affinity measurements in addition to routine screening. We hereby present the development of a set of software tools within the MestReNova (Mnova) package for analyzing 2D NMR for FBDD and hit validation purposes. The package covers three main tasks: (1) unsupervised profiling of raw data to identify outlier data points to exclude in subsequent analyses; (2) batch processing of single-point spectra to identify and rank binders based on chemical shift perturbations or spectral peak intensity changes; and (3) batch processing of multiple titration series to derive binding affinities (KD) by tracing the changes in peak locations or measuring global spectral changes. Toward this end, we implemented and evaluated a set of algorithms for automated peak tracing, spectral binning, and variance analysis by PCA, and a new tool for spectral data intensity comparison using ECHOS. The accuracy and speed of the tools are demonstrated on 2D NMR binding data collected on ligands used in the development of potential inhibitors of the anti-apoptotic MCL-1 protein.

Application of multiplet structure deconvolution to extract scalar coupling constants from 1D nuclear magnetic resonance spectra.

Multiplet structure deconvolution provides a robust method to determine the values of the coupling constants in first-order 1D nuclear magnetic resonance (NMR) spectra. Functions simplifying the coupling structure for partners with spin larger than 1/2 and for doublets with unequal amplitudes were introduced. The chemical shifts of the coupling partners causing mild second-order effects can, in favourable cases, be calculated from the slopes measured in doublet structures. Illustrations demonstrate that deconvolution can straightforwardly analyse multiplet posing difficulties to humans and, in some cases, extract coupling constants from unresolved multiplets.

A KNIME Workflow for Automated Structure Verification.

Adequate characterization of chemical entities made for biological screening in the drug discovery context is critical. Incorrectly characterized structures lead to mistakes in the interpretation of structure-activity relationships and confuse an already multidimensional optimization problem. Mistakes in the later use of these compounds waste money and valuable resources in a discovery process already under cost pressure. Left unidentified, these errors lead to problems in project data packages during quality review. At worst, they put intellectual property and patent integrity at risk. We describe a KNIME workflow for the early and automated identification of these errors during registration of a new chemical entity into the corporate screening catalog. This Automated Structure Verification workflow provides early identification (within 24 hours) of missing or inconsistent analytical data and therefore reduces any mistakes that inevitably get made. Automated identification removes the burden of work from the chemist submitting the compound into the registration system. No additional work is required unless a problem is identified and the submitter alerted. Before implementation, 14% of samples within the existing sample catalog were missing data on initial pass. A year after implementation, only 0.2% were missing data.

NMR signal processing, prediction, and structure verification with machine learning techniques.

Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution.

Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data.

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

An algorithm for discovery and determination of exponentially decaying components in nuclear magnetic resonance relaxometry data.

In many branches of physics, the time evolution of various quantities measured in systems passing from excited to equilibrium states, while theoretically very complex, can be in practice well approximated by a sum of exponential decays. Multiexponential relaxometry data analysis is about determining the number of exponential components and their corresponding amplitudes and decay rates, starting from noisy recorded time series, under the assumption of the discreteness of the number of components present. A technique for decomposing a signal modelled as a sum of exponential decays into its components is introduced, consisting of a modified version of the algorithm minimum description length (MDL) + matrix pencil, originally proposed by Lin et al. for the analysis of nuclear magnetic resonance spectroscopy data. The procedure starts by denoising the discrete time-domain signal, and then a number of different decimations are applied, each being followed by an MDL + matrix pencil detection-estimation step, and finally, a postprocessing of the intermediate outcomes is done. The comprised model order estimator eliminates the need of providing prior estimates of the number of components present.

Applications of the Whittaker smoother in NMR spectroscopy.

The Whittaker smoother, a special case of penalized least square, is a multipurpose algorithm that has proven to be very useful in many scientific fields, including image processing, chromatography, and optical spectroscopy. It shares many similarities with the Savitzky-Golay algorithm, but it is significantly faster and easier to automate. Its use in nuclear magnetic resonance, however, is not widespread although several applications have recently been published. In this review, the mathematical background of the method and its main applications in nuclear magnetic resonance spectroscopy will be discussed.

Application and Methodology of the Non-destructive 19F Time-domain NMR Technique to Measure the Content in Fluorine-containing Drug Products.

Here, we describe a protocol developed by our group that uses low-field fluorine-19 (19F) time-domain (TD) nuclear magnetic resonance (NMR) to measure the average content of fluorinated drugs in their formulated drug product forms: tablets or capsules. This method is specific to fluorinated drugs because it detects only the content of fluorine, avoiding interference from the excipients that lack fluorine. The advantages of measuring the active content of fluorinated drugs using low-field 19F TD-NMR versus high-field 19F solid-state (SS) NMR are the simplicity of the method; the low cost; and the non-destructive nature of the technique, with all samples recoverable in intact forms (e.g., powders, tablets, and capsules), making this technique affordable for any laboratory. We have tested the method with three fluorinated drug products available on the market - cinacalcet, lansoprazole, and ciprofloxacin - with doses ranging from 15 to 500 mg. The results of the analyses, measured by low-field 19F TD-NMR, supported the reported label claims for the average drug content. Based on the simplicity and reproducibility of the analysis, we envision this methodology being implemented in any laboratory, including manufacturing plants, as a process analytical technology (PAT) tool in the pharmaceutical industry.

LocMAP: A new localization method for the parametric processing of high resolution NMR data.

High resolution NMR spectroscopy offers a large number of data points that enable close peaks to be resolved. Data processing algorithms, however, have not yet been able to capitalize on this offering to achieve the highest permissible resolution. Although singular value decomposition (SVD) based methods such as matrix pencil (MPM) are theoretically able to achieve this, their onerous computational cost makes them impractical. In this work, we address this problem and propose a localized MPM method that we refer to as LocMaP, which is capable of delivering the promised high resolution while at the same time taking advantage of the computational efficiency of the FFT. We present the derivation of LocMaP and offer an efficient implementation of it. Evaluation using both Monte Carlo runs and a simulated FID establish the great potential of the proposed method.

Enabling Fast Pseudo-2D NMR Spectral Acquisition for Broadband Homonuclear Decoupling: The EXACT NMR Approach.

Pseudo-2D NMR spectroscopy provides a means of acquiring broadband homonuclear decoupled spectra useful for structural characterization of complex molecules. However, data points concatenated in the direct dimension in these experiments are acquired over incremented time periods-leading to long acquisition times with no sensitivity benefits due to the absence of signal averaging between scans. Herein, the concept of EXACT NMR spectroscopy ("burst" non-uniform sampling of data points) is explored in pseudo-2D experiments with results revealing little or no loss in spectral quality or signal intensity despite the acceleration of acquisition-up to 400 % in some cases.

A robust, general automatic phase correction algorithm for high-resolution NMR data.

The trends towards rapid NMR data acquisition, automated NMR spectrum analysis, and data processing and analysis by more naïve users combine to place a higher burden on data processing software to automatically process these data. Downstream data analysis is compromised by poor processing, and the automated processing algorithms must therefore be robust and accurate. We describe a new algorithm for automatic phase correction of frequency-domain, high-resolution NMR spectra. We show this to be reliable for data derived from a wide variety of typical NMR usages. We therefore conclude that the method will have wide-spread applicability and a positive impact on automated spectral processing and analysis. Copyright © 2017 John Wiley & Sons, Ltd.

1JCH NMR Profile: Identification of Key Structural Features and Functionalities by Visual Observation and Direct Measurement of One-Bond Proton-Carbon Coupling Constants.

A user-friendly NMR interface for the visual and accurate determination of experimental one-bond proton-carbon coupling constants (1JCH) in small molecules is presented. This intuitive 1JCH profile correlates directly to δ(1H), and 1JCH facilitates the rapid identification and assignment of 1H signals belonging to key structural elements and functional groups. Illustrative examples are provided for some target molecules, including terminal alkynes, strained rings, electronegative substituents, or lone-pair-bearing heteronuclei.

Access to experimentally infeasible spectra by pure-shift NMR covariance.

Covariance processing is a versatile processing tool to generate synthetic NMR spectral representations without the need to acquire time-consuming experimental datasets. Here we show that even experimentally prohibited NMR spectra can be reconstructed by introducing key features of a reference 1D CHn-edited spectrum into standard 2D spectra. This general procedure is illustrated with the calculation of experimentally infeasible multiplicity-edited pure-shift NMR spectra of some very popular homonuclear (ME-psCOSY and ME-psTOCSY) and heteronuclear (ME-psHSQC-TOCSY and ME-psHMBC) experiments.

EXtended ACquisition Time (EXACT) NMR-A Case for 'Burst' Non-Uniform Sampling.

A strong case exists for the introduction of burst non-uniform sampling (NUS) in the direct dimension of NMR spectroscopy experiments. The resulting gaps in the NMR free induction decay can reduce the power demands of long experiments (by switching off broadband decoupling for example) and/or be used to introduce additional pulses (to refocus homonuclear coupling, for example). The final EXtended ACquisition Time (EXACT) spectra are accessed by algorithmic reconstruction of the missing data points and can provide higher resolution in the direct dimension than is achievable with existing non-NUS methods.

Exploring the use of Generalized Indirect Covariance to reconstruct pure shift NMR spectra: Current Pros and Cons.

The current Pros and Cons of a processing protocol to generate pure chemical shift NMR spectra using Generalized Indirect Covariance are presented and discussed. The transformation of any standard 2D homonuclear and heteronuclear spectrum to its pure shift counterpart by using a reference DIAG spectrum is described. Reconstructed pure shift NMR spectra of NOESY, HSQC, HSQC-TOCSY and HSQMBC experiments are reported for the target molecule strychnine.

Fast and Efficient Fragment-Based Lead Generation by Fully Automated Processing and Analysis of Ligand-Observed NMR Binding Data.

NMR binding assays are routinely applied in hit finding and validation during early stages of drug discovery, particularly for fragment-based lead generation. To this end, compound libraries are screened by ligand-observed NMR experiments such as STD, T1ρ, and CPMG to identify molecules interacting with a target. The analysis of a high number of complex spectra is performed largely manually and therefore represents a limiting step in hit generation campaigns. Here we report a novel integrated computational procedure that processes and analyzes ligand-observed proton and fluorine NMR binding data in a fully automated fashion. A performance evaluation comparing automated and manual analysis results on (19)F- and (1)H-detected data sets shows that the program delivers robust, high-confidence hit lists in a fraction of the time needed for manual analysis and greatly facilitates visual inspection of the associated NMR spectra. These features enable considerably higher throughput, the assessment of larger libraries, and shorter turn-around times.

Improving the Performance of High-Precision qNMR Measurements by a Double Integration Procedure in Practical Cases.

Quantitative (1)H NMR (qNMR) is a widely applied technique for compound concentration and purity determinations. The NMR spectrum will display signals from all species in the sample, and this is generally a strength of the method. The key spectral determination is the full and accurate determination of one or more signal areas. Accurate peak integration can be an issue when unrelated peaks resonate in an important integral region. We describe a "hybrid" approach to signal integration that provides an accurate estimation of signal area, removing the component(s) that may arise from unrelated peaks. This is achieved by using the most accurate integration method for the region and removing unwanted contributions. The key to this performing well, and in almost all cases, is the use of areas from deconvolved peaks. We describe this process and show that it can be very successfully applied to cases where the highest precision is required and for more common cases of NMR-based quantitation.

Application of (19) F time-domain NMR to measure content in fluorine-containing drug products.

It is necessary to show that the active content in the dosage form of drugs is within a certain narrow range of the label claim. In case of fluorinated drugs, the active content can be measured by high field solid state NMR because the excipients lack fluorine. To make NMR reachable to any laboratory, simple to use, and at a low cost, measurement of (19) F nucleus using a 23 MHz (for (1) H) low field benchtop time-domain (TD) NMR was investigated. Three fluorinated drug products, cinacalcet, lansoprazole, and ciprofloxacin, were chosen for this study. The doses for these drug products range from 15 to 500 mg. The average drug content measured using (19) F TD-NMR compares well with the reported label claims for the three drugs tested. (19) F TD-NMR is a simple and non-destructive technique to measure drug content in tablets. In addition, the accessibility and simplicity of the technique makes it an excellent process analytical technology tool for development and manufacturing in the pharmaceutical industry. Copyright © 2015 John Wiley & Sons, Ltd.