Comparer des méthodes

	Modélisation de pharmacophores	Topologie de réseau d'interactions protéine-protéine	QSAR
Domaine	Bio-informatique	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1977	2000	1964
Auteur d'origine	Peter Gund	Peter Uetz	Corwin Hansch
Type	Pattern-based virtual screening pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
Source fondatrice	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	pharmacophore pattern recognition, 3D pharmacophore	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
Apparentées	3	3	3
Résumé	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.