Comparar métodos

	Modelado por Homología	Topología de Redes de Interacción Proteína-Proteína (PPI)	QSAR
Campo	Bioinformática	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	1993	2000	1964
Autor original	Andrej Sali	Peter Uetz	Corwin Hansch
Tipo	Comparative structure prediction pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
Fuente seminal	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.
Alias	comparative modeling, template-based modeling	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
Relacionados	4	3	3
Resumen	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.