Modeling the geometric, electronic, and redox properties of iron(III)-containing amphiphiles with asymmetric [NN'O] headgroups

Two iron(III)-containing amphiphiles 1 and 2 have been synthesized with the [NN'O] ligands HL(tBu-ODA) (2-((octadecyl(pyridin-2-ylmethyl)amino)methyl)-4,6-di-tert-butylphenol) and HL(I-ODA) (2-((octadecyl(pyridin-2-ylmethyl)amino)methyl)-4,6-diiodophenol), respectively. Compound 1 is monometallic, whereas EXAFS data suggest that 2 is a mixture of mono- and bimetallic species. The archetypical [Fe(III)(L(NN'O))(2)](+) complexes 3-9 have been isolated and characterized in order to understand the geometric, electronic, and redox properties of the amphiphiles. Preference for a monometallic or bimetallic nuclearity is dependent on (i) the nature of the solvent used for synthesis and (ii) the type of the substituent in the phenol moiety. In methanol, the tert-butyl-, methoxy-, and chloro-substituted 3, 4, and 5 are monometallic species, whereas the bromo- and iodo-substituted 6 and 7 form bimetallic complexes taking advantage of stabilizing methoxo bridges generated by solvent deprotonation. In dichloromethane, the bromo- and iodo-substituted 8 and 9 are monometallic species; however, these species favor meridional coordination in opposition to the facial coordination observed for the tert-butyl- and methoxy-substituted compounds. Molecular structures for species 5, 7, 8, and 9 have been solved by X-ray diffraction. Furthermore, the electronic spectrum of the amphiphile 1 was expected to be similar to those of facial/cis archetypes with similar substituents, but close resemblance was observed with the profile for those meridional/cis species, suggesting a similar coordination mode. This trend is discussed based on DFT calculations, where preference for the meridional/cis coordination mode appears related to the presence of tertiary amine nitrogen on the ligand, as when a long alkyl chain is attached to the [NN'O] headgroup.     

Practical syntheses of a CXCR3 antagonist

Two new, reliable syntheses of a pyrido[2,3-d]-pyrimidine inhibitor of the CXCR3 receptor are described. A nine-step synthesis of the CXCR3 inhibitor (1) from 2-aminonicotinic acid was demonstrated on a multikilogram scale and incorporates a classic resolution to deliver the enantioenriched active pharmaceutical ingredient (API). A second synthesis of the CXCR3 inhibitor starts from (+)-(D)-Boc alanine and 2-chloronicotinic acid and utilizes a Goldberg coupling. This second synthesis, performed on a gram scale, intersects the former route at a common intermediate thereby completing a formal synthesis of the enantioenriched API in higher overall yield without the need for a resolution.     

Reaktivitäut und reaktionswege von methylsubstituierten bisindolylcarbenium-ionen

Methyl substituted bisindolylcarbenium ions 1 react with some O- and C-nucleophiles regioselectively. The cations 1b, 1c yield with hydroxide ions the tetraindolyldimethyl ether 4 and with methoxide ions the bisin-dolylmethoxymethanes 5. Compounds 1a, 1b, 1c react with several methylindoles to isomeric bis- and trisin-dolylmethanes. An electrophilic reactivity order of cations 1 can be derived supporting on the experimental results.     

Ueber die Beziehungen von Neutralsalz zu Säureeiweiß

Ohne Zusammenfassung     

Neural Networks in Chemistry

The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's various functions. At the same time the development of computers was taking a totally different direction. As a result, today's computer architectures, operating systems, and programming have very little in common with information processing as performed by the brain. Currently we are experiencing a reevaluation of the brain's abilities, and models of information processing in the brain have been translated into algorithms and made widely available. The basic building-block of these brain models (neural networks) is an information processing unit that is a model of a neuron. An artificial neuron of this kind performs only rather simple mathematical operations; its effectiveness is derived solely from the way in which large numbers of neurons may be connected to form a network. Just as the various neural models replicate different abilities of the brain, they can be used to solve different types of problem: the classification of objects, the modeling of functional relationships, the storage and retrieval of information, and the representation of large amounts of data. This potential suggests many possibilities for the processing of chemical data, and already applications cover a wide area: spectroscopic analysis, prediction of reactions, chemical process control, and the analysis of electrostatic potentials. All these are just a small sample of the great many possibilities.