Gas-Phase Lithium Cation Affinity of Glycine

2015 ◽  
Vol 21 (3) ◽  
pp. 149-159 ◽  
Author(s):  
Sophie Bourcier ◽  
Ru Xuan Chia ◽  
Rosa Ngo Biboum Bimbong ◽  
Guy Bouchoux
1999 ◽  
Vol 5 (1) ◽  
pp. 259 ◽  
Author(s):  
Marta Herreros ◽  
Jean- François Gal ◽  
Pierre- Charles Maria ◽  
Michèle Decouzon

2007 ◽  
Vol 80 (1) ◽  
pp. 195-203 ◽  
Author(s):  
Hideaki Maeda ◽  
Maki Irie ◽  
Soe Than ◽  
Kiyoshi Kikukawa ◽  
Masaaki Mishima

2017 ◽  
Vol 121 (45) ◽  
pp. 8706-8718 ◽  
Author(s):  
Ewa D. Raczyńska ◽  
Jean-François Gal ◽  
Pierre-Charles Maria ◽  
Piotr Michalec ◽  
Marcin Zalewski
Keyword(s):  

2012 ◽  
Vol 89 (11) ◽  
pp. 1476-1478 ◽  
Author(s):  
Jean-François Gal ◽  
Charly Mayeux ◽  
Lionel Massi ◽  
Mohamed Major ◽  
Laurence Charles ◽  
...  

2000 ◽  
Vol 104 (12) ◽  
pp. 2824-2833 ◽  
Author(s):  
Peeter Burk ◽  
Ilmar A. Koppel ◽  
Ivar Koppel ◽  
Riho Kurg ◽  
Jean-Francois Gal ◽  
...  

2009 ◽  
Vol 74 (1) ◽  
pp. 217-241 ◽  
Author(s):  
Alan R. Katritzky ◽  
Yueying Ren ◽  
Svetoslav H. Slavov ◽  
Mati Karelson

Correlation of gas-phase lithium cation basicities (LCB) of 259 diverse compounds extends the published datasets utilizing multilinear, support vector machine (SVM) and projection pursuit regression (PPR) modeling. The best multiple linear regression (BMLR) method implemented in CODESSA was used to: (i) build multiparameter linear QSPR models and (ii) select set of descriptors for further treatment by the SVM and PPR. The external predictivity and the performance of each of the above methods was estimated and compared to those of the other techniques. The PPR method produced results superior to SVM, which in turn outperformed MLR. The physico-chemical interpretation of each of the descriptors provides new insight into the mechanism of LCB interactions.


2007 ◽  
Vol 267 (1-3) ◽  
pp. 315-323 ◽  
Author(s):  
M. Hallmann ◽  
E.D. Raczyńska ◽  
J.-F. Gal ◽  
P.-C. Maria
Keyword(s):  

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