Comparar métodos

	Modelagem por Homologia	Modelagem de Farmacóforo	QSAR
Área	Bioinformática	Bioinformática	Bioinformática
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1993	1977	1964
Autor original	Andrej Sali	Peter Gund	Corwin Hansch
Tipo	Comparative structure prediction pipeline	Pattern-based virtual screening pipeline	Regression-based predictive modeling pipeline
Fonte seminal	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	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.	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.
Outros nomes	comparative modeling, template-based modeling	pharmacophore pattern recognition, 3D pharmacophore	QSAR model, quantitative structure-activity relationship
Relacionados	4	3	3
Resumo	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.	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.	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.