পদ্ধতির তুলনা করুন

	মলিকুলার ডকিং	প্রোটিন-প্রোটিন মিথস্ক্রিয়া নেটওয়ার্ক টপোলজি	কোয়ান্টিটেটিভ স্ট্রাকচার-অ্যাক্টিভিটি রিলেশনশিপ (QSAR)
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1982	2000	1964
প্রবর্তক	Irwin Kuntz	Peter Uetz	Corwin Hansch
ধরন	Binding prediction pipeline	Network analysis pipeline	Regression-based predictive modeling pipeline
মৌলিক উৎস	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.
অপর নাম	protein-ligand docking, binding prediction	protein interaction networks, interactome analysis, network topology	QSAR model, quantitative structure-activity relationship
সম্পর্কিত	4	3	3
সারসংক্ষেপ	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.