Porovnat metody

	Homologní modelování	Topologie PPI sítí	QSAR
Obor	Bioinformatika	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1993	2000	1964
Tvůrce	Andrej Sali	Peter Uetz	Corwin Hansch
Typ	Comparative structure prediction pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
Původní zdroj	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	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.
Další názvy	comparative modeling, template-based modeling	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
Příbuzné	4	3	3
Shrnutí	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.	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.