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.
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.
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. With a variety of BT NMR systems currently available (e.g., 43, 60, 80, and 100 MHz), it can be overwhelming for instructors to determine which system is appropriate for their needs. When used as a structure elucidation tool, the focus is often placed solely on solving chemical structures, prompting the eventual question of the magnetic field strength requirements for de novo structure elucidation. To answer this question, two artificial intelligence (AI) software packages, namely Structural Elucidator (v.2020.1.2) from ACD/ Laboratories and Mnova Structure Elucidation (v 14.2.3) from Mestrelab Research, were used. These software provide an unbiased, yet separate, metric to gauge the effect of magnetic field strength on the accuracy of the determined structures. For comparison purposes, results from these two BT magnetic field strengths will be compared to those obtained from a high field NMR (500 MHz) spectrometer, providing a complete overview of the advances, as well as limitations in current BT systems for undergraduate education. In addition, the spectral data presented in this work can be used as a practical example in class to illustrate the effect of spectral resolution on the accuracy of determined structures, which is fundamental to understanding structure elucidation within organic chemistry.
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