Papers and Peer Reviewed Publications

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.