Сравнение на методи

	Хомоложно моделиране	Молекулярно докиране	Фармакофорно моделиране
Област	Биоинформатика	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Година на възникване	1993	1982	1977
Създател	Andrej Sali	Irwin Kuntz	Peter Gund
Тип	Comparative structure prediction pipeline	Binding prediction pipeline	Pattern-based virtual screening pipeline
Основополагащ източник	Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.	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.	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.
Други названия	comparative modeling, template-based modeling	protein-ligand docking, binding prediction	pharmacophore pattern recognition, 3D pharmacophore
Свързани	4	4	3
Резюме	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.	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.	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.