Сравнение методов

	Молекулярное докирование	Топология сети белок-белковых взаимодействий	QSAR
Область	Биоинформатика	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1982	2000	1964
Автор метода	Irwin Kuntz	Peter Uetz	Corwin Hansch
Тип	Binding prediction pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
Основополагающий источник	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.	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.
Другие названия	protein-ligand docking, binding prediction	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
Связанные	4	3	3
Сводка	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.	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.