Inhibitors of prolyl endopeptidase: Characterization of the pharmacophoric pattern using conformational analysis and 3D-QSAR

Summary A structure-activity study has been carried out on several compounds known as inhibitors of the serine protease prolyl endopeptidase. Conformational analysis has been done using different molecular mechanics methods such as molecular dynamics, or a randomized conformational search method. The conformers obtained were classified using geometric and energetic criteria. A pattern recognition analysis was done in order to divide conformers according to families. The resulting dominant families, for all compounds investigated, showed very similar geometric features. Based on the lowest energy conformers obtained after randomized conformational analysis, a 3D-QSAR model was established using the CoMFA approach. The validity of this model was verified by prediciting correctly the activity of other molecules not used in the construction of this model.     

Catalytic stereoselective alkene aziridination with sulfonimidamides

Diastereoselective copper-catalyzed alkene aziridination has been investigated using chiral nitrenes generated from sulfonimidamides in the presence of an iodine(III) oxidant. Starting from a stoichiometric amount of the substrates, the corresponding aziridines were isolated with excellent yields of up to 96%. Good levels of asymmetric induction were obtained in the case of electron-poor olefins, with an optimal de of 94% being reached starting from tert-butyl acrylate. Matching and mismatching effects were also observed upon the use of chiral copper catalysts for the aziridination of styrene.     

New Procedure for the Preparation of the Tetrazole Analogue of GABA

The hydrochloride of the tetrazolyl bioisostere of the neurotransmitter GABA can be easily prepared by means of three simple steps: a Gabriel-type substitution on 4-bromobutyronitrile with Boc₂NH, a tetrazole formation by action of azidotributyltin on the cyano group and a deprotection step using gaseous HCl.     

Micro-level reserving for general insurance claims using a long short-term memory network

Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to use micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values.     

Studies in catalytic C-H amination involving nitrene C-H insertion

Stereoselective catalytic intermolecular C-H amination of complex molecules is reported. Site-selective functionalizations occur with very good yields up to 91% and excellent d.e.s up to 99%. However, the precise nature of the nitrene C-H insertion remains a matter of debate despite several physical organic experiments.     

Synthesis and characterization of nanoheterostructures based on oligothiophene functionalized Ru nanoparticles

Monodisperse ruthenium nanoparticles functionalized by electroactive oligothiophenes have been prepared and characterized. Using TEM, UV-visible and FTIR we established that the organization of these nanoparticles into nanospheres can be directly controlled via modulation of the pi-pi interaction between the organic components adsorbed on the surface. This finding also shows that the self-assembled nanoheterostructures may be switched from monodisperse nanoparticles to ordered nanospheres by tuning the pH.