Comparer des méthodes

	Dockage moléculaire	Modélisation de pharmacophores	Topologie de réseau d'interactions protéine-protéine
Domaine	Bio-informatique	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1982	1977	2000
Auteur d'origine	Irwin Kuntz	Peter Gund	Peter Uetz
Type	Binding prediction pipeline	Pattern-based virtual screening pipeline	Network analysis pipeline
Source fondatrice	Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 161(2), 269-288.	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.
Alias	protein-ligand docking, binding prediction	pharmacophore pattern recognition, 3D pharmacophore	protein interaction networks, interactome analysis, network topology
Apparentées	4	3	3
Résumé	Molecular docking predicts the preferred binding orientation and affinity of a ligand (small molecule) within a protein binding pocket. Pioneered by Kuntz and colleagues in 1982, this computational method searches conformational space to find energetically favorable ligand-protein complexes, enabling rapid screening of chemical libraries for drug discovery.	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.