PHOTOREACTIVATION OF THYMINE-STARVED ESCHERICHIA COLIBs-1

Abstract —Thymine starvation prior to 254 nm ultraviolet light (UV) exposures has been found to decrease the level of maximum photoreactivation in Escherichia coli B _s-1. The dark equilibrium level of photoreactivating enzyme-substrate complexes was determined from the levels of photoreactivation obtained with exposures to single flashes of high-intensity light. The kinetics indicate that photoreactivating enzyme concentration does not decrease as a result of thymine starvation. The UV sensitivities of normal and thymine-starved cells are found to be the same. Photoreactivation by sequential flashes shows a lesser number of total photorepairable lesions in starved cells. It is concluded that thymine starvation renders a portion of the dimers inaccessible to the photoreactivating enzyme, thus lowering the level of maximum photoreactivation.     

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol⁻¹ were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

A machine learning model for enantioselectivity prediction using reaction-based molecular representations.     

Microwave assisted synthesis of an unusual dinitro phytochemical

A novel dinitro secondary metabolite, 2-nitro-4-(2^′-nitroethenyl)phenol from a marine source, has been prepared via highly accelerated, microwave assisted, nitration reactions using mild reagents. ipso-Substitution of a carboxy group by a nitro group is discussed.