Fragmentation pattern of amides by EI and HRESI: study of protonation sites using DFT-3LYP data.

Amides are important natural products which occur in a few plant families. Piplartine and piperine, major amides in Piper tuberculatum and P. nigrum, respectively, have shown a typical N-CO cleavage when analyzed by EI-MS or HRESI-MS. In this study several synthetic analogs of piplartine and piperine were subjected to both types of mass spectrometric analysis in order to identify structural features influencing fragmentation. Most of the amides showed an intense signal of the protonated molecule [M + H]+ when subjected to both HRESI-MS and EI-MS conditions, with a common outcome being the cleavage of the amide bond (N-CO). This results in the loss of the neutral amine or lactam and the formation of aryl acylium cations. The mechanism of N-CO bond cleavage persists in α,ß-unsaturated amides because of the stability caused by extended conjugation. Computational methods determined that the protonation of the piperamides and their derivatives takes place preferentially at the amide nitrogen supporting the dominant the N-CO bond cleavage.

Expert system for predicting reaction conditions: the Michael reaction case.

A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael ß-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html . Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eNovalys database (originally from patent literature).

Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods.

A recent approach based on self-organizing maps (SOMs) to extract patterns from three-way data, named MOLMAP, was applied in a four-seasons study on soil pollution and its results compared with three different conventional approaches: Parallel factor analysis (PARAFAC), matrix augmented principal components analysis (MA-PCA) and Procrustes rotation. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Zn, loss on ignition, pH and humidity). It was found that all techniques yielded highly similar results as the samples became organized in two major groups, each with a differentiated pollution pattern. This confirmed MOLMAP as a reliable option to handle environmental three-way datasets and to extract accurate pollution patterns.

New description of molecular chirality and its application to the prediction of the preferred enantiomer in stereoselective reactions.

A new representation of molecular chirality as a fixed-length code is introduced. This code describes chiral carbon atoms using atomic properties and geometrical features independent of conformation and is able to distinguish between enantiomers. It was used as input to counterpropagation (CPG) neural networks in two different applications. In the case of a catalytic enantioselective reaction the CPG network established a correlation between the chirality codes of the catalysts and the major enantiomer obtained by the reaction. In the second application-enantioselective reduction of ketones by DIP-chloride-the series of major and minor enantiomers produced from different substrates were clustered by the CPG neural network into separate regions, one characteristic of the minor products and the other characteristic of the major products.