Papers and Peer Reviewed Publications

In vivo NMR is evolving into an important tool to understand biological processes and environmental responses. Current approaches use flow systems to sustain the organisms with oxygenated water and food (e.g., algae) inside the NMR.

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

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation.

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.

The recent popularity of benchtop (BT) NMR systems has prompted its applications in undergraduate laboratories around the world. Owing to their low maintenance cost, due to the lack of a superconducting magnetic core, and simple operation, these BT NMR systems can fulfill many of the learning objectives outlined in the undergraduate organic chemistry curricula.

One of the interesting initiatives discussed during the IUPAC General Assembly a few weeks ago in Sao Paulo was the renewed push for more efficient and simpler ways of submitting supplementary spectroscopic data.

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.

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.

Spectroscopic non-targeted methods are gaining ever-growing importance in quality control and authenticity assessment of food products because of their strong potential for identification of specific features of the products by data-driven classifiers.

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.

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.

Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules.

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.

Pseudo-2D NMR provides a means of acquiring broadband homonuclear decoupled spectra useful for structural characterization of complex molecules.

Computer-assisted methods development (CAMD) of LC (liquid chromatography) separations comprises the strategies, methodologies, and software that can be used for more effective and efficient development of separations needed in the analytical chromatography laboratory.

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.

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.

The automatic analysis of NMR data has been a much-desired endeavour for the last six decades, as it is the case with any other analytical technique.

Since the first experiments on the detection of nuclear resonance signals, NMR spectroscopy has been successfully used as quantitative technique applied to several kind of matrixes.

Multiple myeloma (MM) is a malignancy of plasma cells characterized by multifocal osteolytic bone lesions. Macroscopic and genetic heterogeneity has been documented within MM lesions. Understanding the bases of such heterogeneity may unveil relevant features of MM pathobiology.

Quantitative 1H NMR (qNMR) is a widely applied technique for compound concentration and purity determinations.

A strong case exists for the introduction of burst non-uniform sampling (NUS) in the direct dimension of NMR spectroscopy experiments.

The current Pros and Cons of a processing protocol to generate pure chemical shift NMR spectra using Generalized Indirect Covariance are presented and discussed.

Covariance processing is a versatile processing tool to generate synthetic NMR spectral representations without the need to acquire time-consuming experimental datasets.

Abnormal metabolism is a key tumor hallmark. Proton magnetic resonance spectroscopy (1H-MRS) allows measurement of metabolite concentration that can be utilized to characterize tumor metabolic changes. 1H-MRS measurements of specific metabolites have been implemented in the clinic.

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 authors describe methods to quickly acquire NUS-assisted 2D spectra that are suitable for qNMR.

Carlos Cobas has written this article about NMR analysis on mobiles devices in collaboration with Isaac Iglesias and Felipe Seoane.

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.

Spectroscopic methods – of which nuclear magnetic resonance (NMR) is one of the most vital players – have almost entirely replaced those old “wet chemistry”-based approaches and the art has become a science.

Covariance NMR has attracted considerable interest in recent years and new applications of this technique aimed at paving the way forward to structure elucidation are appearing frequently in the scientific literature.