Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules.

Algorithmically identifying the meaningful similarities between an assortment of molecules is a critical chemical problem, and one which is only gaining in relevance as data-driven chemistry continues to progress. Effectively addressing this challenge can be achieved through a reformulation of the problem into information theory, cluster-based supervised classification, and the implementation of key concepts, particularly information entropy and mutual information. These concepts are combined with unsupervised learning atop learned chemical spaces to generate meaningful labels for arbitrary collections of molecules.

Deep Learning in Chemistry.

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning.

Effect of heteroatom and functionality substitution on the oxidation potential of cyclic nitroxide radicals: role of electrostatics in electrochemistry.

The oxidation potential of a test set of 21 nitroxide radicals, including a number of novel compounds, has been studied experimentally in acetonitrile and correlated with theoretical calculations. It was found that both Hammett constants (σ) of the substituents on the nitroxide radicals and hyperfine splitting constants of the respective nitrogen atoms (α) were well correlated to their experimental oxidation potentials. Theoretical calculations, carried out at the G3(MP2,CC)(+)//M06-2X/6-31+G(d,p) level of theory with PCM solvation corrections, were shown to reproduce experiments to within a mean absolute deviation of 33 mV, with a maximum deviation of 64 mV.