Uporedite metode

	Farmakoforno modelovanje	Molekulsko spajanje	QSAR
Oblast	Bioinformatika	Bioinformatika	Bioinformatika
Porodica	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	1977	1982	1964
Tvorac	Peter Gund	Irwin Kuntz	Corwin Hansch
Tip	Pattern-based virtual screening pipeline	Binding prediction pipeline	Regression-based predictive modeling pipeline
Temeljni izvor	Wermuth, C. G., Ganellin, C. R., Lindberg, P., & Mitscher, L. A. (1998). Glossary of terms used in medicinal chemistry. Pure and Applied Chemistry, 70(5), 1129-1143.	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.
Drugi nazivi	pharmacophore pattern recognition, 3D pharmacophore	protein-ligand docking, binding prediction	QSAR model, quantitative structure-activity relationship
Srodne	3	4	3
Sažetak	Pharmacophore modeling identifies the spatial arrangement of molecular features (hydrogen bond donors, acceptors, aromatic rings) that are essential for biological activity. Introduced by Gund in 1977, this ligand-based method creates a three-dimensional pattern that can screen chemical libraries and design new active compounds without requiring receptor structure.	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.