Comparer des méthodes

	Modélisation par homologie	Modélisation de pharmacophores	Topologie de réseau d'interactions protéine-protéine	QSAR
Domaine	Bio-informatique	Bio-informatique	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1993	1977	2000	1964
Auteur d'origine	Andrej Sali	Peter Gund	Peter Uetz	Corwin Hansch
Type	Comparative structure prediction pipeline	Pattern-based virtual screening pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
Source fondatrice	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	Wermuth, C. G., Ganellin, C. R., Lindberg, P., & Mitscher, L. A. (1998). Glossary of terms used in medicinal chemistry. Pure and Applied Chemistry, 70(5), 1129-1143.	Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., ... & Lomax, J. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-627.	Hansch, C. & Fujita, T. (1964). Rho-sigma-pi analysis. A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616-1626.
Alias	comparative modeling, template-based modeling	pharmacophore pattern recognition, 3D pharmacophore	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
Apparentées	4	3	3	3
Résumé	Homology modeling, also called comparative modeling, predicts the three-dimensional structure of a protein using an experimentally-solved structure of a homologous protein as a template. Introduced by Sali and Blundell in 1993, this method exploits the principle that homologous proteins share similar spatial structures despite differing in amino acid sequence.	Pharmacophore modeling identifies the spatial arrangement of molecular features (hydrogen bond donors, acceptors, aromatic rings) that are essential for biological activity. Introduced by Gund in 1977, this ligand-based method creates a three-dimensional pattern that can screen chemical libraries and design new active compounds without requiring receptor structure.	Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organization and disease associations.	Quantitative Structure-Activity Relationship (QSAR) modeling predicts biological activity from molecular structure using statistical or machine learning models. Pioneered by Hansch in 1964, QSAR correlates numerical molecular descriptors with measured bioactivity, enabling prediction of activity for untested compounds and rational lead optimization.