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2020
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.
2019
We had a very exciting webinar about a hot topic in the NMR field, Higher Order Structure (HOS) analysis of biotherapeutic proteins. Dr. Donna Baldisseri (Bruker) described recently developed and optimized acquisition techniques. Dr. Mike Bernstein (Mestrelab) showed how data can be easily processed and analysed with the recently launched Mnova BioHOS plugin
Therapeutic pharmaceuticals (drugs) have witnessed a sea change in recent years. The large, dominant group of drugs derived from synthetic, small molecules has been joined by a new type of drug that has been very effective with diseases that were previously untreatable.
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.
