Krahasoni metodat

	Modelimi i Homologjisë	Lidhja Molekulare	QSAR
Fusha	Bioinformatikë	Bioinformatikë	Bioinformatikë
Familja	Process / pipeline	Process / pipeline	Process / pipeline
Viti i origjinës	1993	1982	1964
Krijuesi	Andrej Sali	Irwin Kuntz	Corwin Hansch
Lloji	Comparative structure prediction pipeline	Binding prediction pipeline	Regression-based predictive modeling pipeline
Burimi themelues	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	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.	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.
Emërtime të tjera	comparative modeling, template-based modeling	protein-ligand docking, binding prediction	QSAR model, quantitative structure-activity relationship
Të lidhura	4	4	3
Përmbledhja	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.	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.	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.