Võrdle meetodeid

	Molekulaarne dokkimine	Homoloogia modelleerimine	QSAR
Valdkond	Bioinformaatika	Bioinformaatika	Bioinformaatika
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1982	1993	1964
Looja	Irwin Kuntz	Andrej Sali	Corwin Hansch
Tüüp	Binding prediction pipeline	Comparative structure prediction pipeline	Regression-based predictive modeling pipeline
Algallikas	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.	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	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.
Rööpnimetused	protein-ligand docking, binding prediction	comparative modeling, template-based modeling	QSAR model, quantitative structure-activity relationship
Seotud	4	4	3
Kokkuvõte	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.	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.	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.