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| QSAR× | Modélisation de pharmacophores× | |
|---|---|---|
| Domaine | Bio-informatique | Bio-informatique |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1964 | 1977 |
| Auteur d'origine≠ | Corwin Hansch | Peter Gund |
| Type≠ | Regression-based predictive modeling pipeline | Pattern-based virtual screening pipeline |
| Source fondatrice≠ | 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. DOI ↗ | 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. DOI ↗ |
| Alias | QSAR model, quantitative structure-activity relationship | pharmacophore pattern recognition, 3D pharmacophore |
| Apparentées | 3 | 3 |
| Résumé≠ | 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. | 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. |
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