Compară metode

	Modelarea farmacoforului	Modelare prin omologie	QSAR
Domeniu	Bioinformatică	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1977	1993	1964
Autorul original	Peter Gund	Andrej Sali	Corwin Hansch
Tip	Pattern-based virtual screening pipeline	Comparative structure prediction pipeline	Regression-based predictive modeling pipeline
Sursa seminală	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.
Denumiri alternative	pharmacophore pattern recognition, 3D pharmacophore	comparative modeling, template-based modeling	QSAR model, quantitative structure-activity relationship
Înrudite	3	4	3
Rezumat	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.