Heteroatom insertions into chemically inert carbon–carbon single bonds are rare compared to their unsaturated analogues. Now, ligand-to-metal charge transfer offers a promising entry point for oxygen ...

We report a catalytic strategy for generating carbon-centered radicals from organoboron compounds under deep-red to near-infrared (DR to NIR) light irradiation via direct excitation of substrate–catal...

2-Iodoxybenzoic acid (IBX) is a versatile oxidant used in chemical synthesis. It has been employed as a reagent in more than 1400 publications since its initial use as an oxidant described in 1994. Wh...

Magnetic stirrers, the most widely used and ubiquitous devices for performing chemical reactions in laboratory settings, may cause reproducibility problems. Reproducibility in a range of chemical proc...

Chemistry education at the graduate level and beyond faces the formidable challenge of a boundless and constantly expanding frontier of knowledge on many fronts. While modern learners have an increasi...

Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at an ever-increasing rate owing to the progressive automation of chemical analysis. This growth and automation have led to the data analysis step in NMR spectroscopy becoming the main bottleneck in the structural characterization of a new chemical compound. In particular, the data interpretation step is slow and prone to error as it requires manual examination by a suitably trained scientist. Machine learning (ML) methods could overcome this bottleneck, pending that they can automatically correlate the collection of peaks in an NMR spectrum with the substructure of its subject molecule. This study explores the art of the possible using three types of ML methods that are based on neural-network architectures: a multilayer perceptron (MLP) + long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and an MLP + recurrent neural network (RNN). NMR spectrum–structure correlations were encoded into each type of neural network using two forms of molecular representation, one employing functional groups and the other using a novel neighbor-based method. These models were trained on 34,503 and 17,311 experimental 13C and 1H NMR spectra, respectively. The influence of incorporating metadata about experimental conditions (NMR field strength, temperature, and solvent) into the neural-network model was also investigated. The models incorporated coupling constants as a proxy for spectral intensities in the case of 13C NMR spectra. We found that the MLP + LSTM model achieved the highest accuracy (88%) when trained on 13C NMR spectra and incorporating experimental metadata (compared to 77% without incorporating it). While the CNN model performance was slightly lower (86% accuracy), it determined molecular substructures in one-third of the computational run time compared to the MLP + LSTM model. Thus, the CNN model emerged as the practically best model when considering performance, time, and cost.

Aryl alkyl sulfones are an important class of compounds in drug discovery; thus, new methods toward their synthesis are desirable. A general sulfur fluoride exchange (SuFEx) method to couple aryl sulf...

Amines and ethers represent essential structural motifs in pharmaceuticals, natural products, organic materials, and catalytic systems. The development of novel, environmentally friendly, and cost-eff...

Copper-mediated transient C─H sulfanylation of benzylamines with sulfenamides enables rapid access to thioethers, cyclic sulfilimines, and sulfoximines. Mechanistic investigations using 1H NMR and el....

48 projects will receive 2.4 million euros from the Open Competition Domain Science- XS round 25-2. The subjects vary from identification of the most effective fungal cutinase for plastic recycling to comparing gene expression and cellular behaviour of these scar-forming cells with cells from pulmonary emboli that do not show scars.

The first study of the high-pressure behavior of organic halogen bond donors in solution is presented: HP-NMR titrations were performed in various solvents, primarily with a neutral tridentate halogen...

Applications are invited for a Postdoctoral Research Associate position to work under the supervision of Prof. Andrew Lawrence on a project funded by the Leverhulme Trust.