Sammenlign metoder

	Farmakofor-modellering	Homologimodellering	QSAR
Fagfelt	Bioinformatikk	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	1977	1993	1964
Opphavsperson	Peter Gund	Andrej Sali	Corwin Hansch
Type	Pattern-based virtual screening pipeline	Comparative structure prediction pipeline	Regression-based predictive modeling pipeline
Opprinnelig kilde	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.	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.
Alias	pharmacophore pattern recognition, 3D pharmacophore	comparative modeling, template-based modeling	QSAR model, quantitative structure-activity relationship
Relaterte	3	4	3
Sammendrag	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.	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.