A
Annick Panaye
Researcher at University of Paris
Publications - 60
Citations - 1224
Annick Panaye is an academic researcher from University of Paris. The author has contributed to research in topics: Steric effects & Quantitative structure–activity relationship. The author has an hindex of 18, co-authored 60 publications receiving 1178 citations. Previous affiliations of Annick Panaye include Centre national de la recherche scientifique & Lanzhou University.
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Journal ArticleDOI
Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.
Xiaojun Yao,Annick Panaye,Jean-Pierre Doucet,Ruisheng Zhang,Hai-Feng Chen,Mancang Liu,Zhide Hu,Bo Tao Fan +7 more
TL;DR: The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies and is comparable or superior to those obtained by MLR and RBFNN.
Journal ArticleDOI
Steric effects—I: A critical examination of the taft steric parameter—Es. Definition of a revised, broader and homogeneous scale. Extension to highly congested alkyl groups
TL;DR: The revised Taft scale is termed E's as discussed by the authors, which includes 44 of the original groups cited by Taft with 50 additional values obtained from literature data and has been extended to extremely hindered alkyl groups (13 in number) by measurement based on competitive reactivity.
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Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design
TL;DR: Its applications in QSPR/QSAR studies, and particularly in drug design are discussed, and comparative studies with some linear and other nonlinear methods show SVMs high performance both in classification and correlation.
Journal ArticleDOI
Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks
Xiaojun Yao,Xiaojun Yao,Annick Panaye,Jean-Pierre Doucet,Hai-Feng Chen,Ruisheng Zhang,Botao Fan,Mancang Liu,Zhi De Hu +8 more
TL;DR: In this paper, the performance and predictive capability of support vector machine (SVM) and radial basis function neural network (RBFNN) for classification problems in QSAR/QSPR were investigated and compared with several other classification methods such as linear discriminant analysis (LDA) and nonlinear discriminate analysis (NLDA).
Journal ArticleDOI
QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines.
Xiaojun Yao,Huanxiang Liu,Ruisheng Zhang,Mancang Liu,Zhide Hu,Annick Panaye,Jean-Pierre Doucet,Botao Fan +7 more
TL;DR: A new and effective method for drug design and screening for a novel series of 1,4-dihydropyridine calcium channel antagonists for the first time using the least squares support vector machine.