J
Jean-Pierre Doucet
Researcher at University of Paris
Publications - 137
Citations - 1591
Jean-Pierre Doucet is an academic researcher from University of Paris. The author has contributed to research in topics: Chemical shift & Hydrogen bond. The author has an hindex of 18, co-authored 137 publications receiving 1539 citations. Previous affiliations of Jean-Pierre Doucet include Lanzhou University & Centre national de la recherche scientifique.
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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.
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Molecular similarity and diversity in chemoinformatics: from theory to applications.
TL;DR: The approaches used to define and descript the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed and the descriptions and analyses of different methods and techniques are introduced.
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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.
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Anti-Toxoplasma Activities of 24 Quinolones and Fluoroquinolones In Vitro: Prediction of Activity by Molecular Topology and Virtual Computational Techniques
Rafael Gozalbes,Monique Brun-Pascaud,Ramón García-Domenech,Jorge Galvez,Pierre-Marie Girard,Jean-Pierre Doucet,Francis Derouin +6 more
TL;DR: The in vitro activity of quinolones against T. gondii was assessed and new quantitative structure-activity relationship models able to predict this activity were developed and showed that the carboxylic group at position C-3 of the qu inolone ring was not essential for anti-Toxoplasma activity.
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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).