Enantiomerically Pure Isophorone Diamine [3-(Aminomethyl)-3,5,5-trimethylcyclohexylamine]:  A Chiral 1,4-Diamine Building Block Made Available on Large Scale

2006 ◽  
Vol 71 (25) ◽  
pp. 9312-9318 ◽  
Author(s):  
Albrecht Berkessel ◽  
Katrin Roland ◽  
Michael Schröder ◽  
Jörg M. Neudörfl ◽  
Johann Lex
2019 ◽  
Vol 17 (1) ◽  
pp. 35-38 ◽  
Author(s):  
Iaroslav Baglai ◽  
Michel Leeman ◽  
Richard M. Kellogg ◽  
Willem L. Noorduin

A simple route to enantiomerically pure (S)-2-aminobutyramide – the chiral component of the anti-epileptic drugs Levetiracetam and Brivaracetam has been developed. This approach is based on the rational design and application of a Viedma ripening process. The practical potential of the process is demonstrated on a large scale.


2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Amina Sadiq ◽  
Norbert Sewald

The ready accessibility of (R)-α-aminoadipic acid by enzymatic cleavage of cephalosporin C (CephC) in the production of 7-aminocephalosporanic acid (7-ACA) on a large scale makes it a favorable chiral pool building block for the synthesis of unusual amino acids. A route for the synthesis of C-5-alkenyl and C-6-alkylidene derivatives of (R)-pipecolic acid is described which utilizes (R)-α-aminoadipic acid as the enantiomerically pure starting material. Moreover, the synthesis of azido and triazolyl derivatives of (R)-α-aminoadipic acid is reported.


2016 ◽  
Vol 81 (9) ◽  
pp. 3961-3966 ◽  
Author(s):  
Adrien Vincent ◽  
Damien Deschamps ◽  
Thomas Martzel ◽  
Jean-François Lohier ◽  
Christopher J. Richards ◽  
...  

ChemInform ◽  
2010 ◽  
Vol 41 (23) ◽  
pp. no-no
Author(s):  
Jefferson L. Princival ◽  
Morilo S. C. de Oliveira ◽  
Alcindo A. Dos Santos ◽  
Joao V. Comasseto

ChemInform ◽  
2010 ◽  
Vol 32 (33) ◽  
pp. no-no
Author(s):  
Derek A. Pflum ◽  
H. Scott Wilkinson ◽  
Gerald J. Tanoury ◽  
Donald W. Kessler ◽  
Hali B. Kraus ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Aboubakar Nasser Samatin Njikam ◽  
Huan Zhao

This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems. This new architecture is composed of four building blocks for feature extraction. Each of these building blocks, except the last one, uses 1 × 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block. In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block. Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net’s superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.


2007 ◽  
Vol 2007 (2) ◽  
pp. 249-257 ◽  
Author(s):  
Genevieve Etornam Adukpo ◽  
Tobias Borrmann ◽  
René Manski ◽  
Rosa I. Sáez Díaz ◽  
Wolf-Dieter Stohrer ◽  
...  

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