Molecular descriptors, QSAR ligands

The quantitative comparison of the optimized 3D structure of a selected set of ligands allows the development of their minimal 3D structural requirements for the recognition and activation of the biological target, that is, the pharmacophore hypothesis, and gives a sound 3D rationale to the available SARs [21]. A more complete and mechanistically relevant approach to the development of the 3D pharmacophore consists in its translation into a numerical molecular descriptor that quantifies the molecular-pharmacophore similarity-diversity for computational QSAR modeling [21,41]. 

Figure 4.1 Ligand-based virtual screening methods. The figure shows different computational methods for screening compound databases that take either a local or a global view on molecular structure. Molecular similarity methods that operate on molecular descriptors, histogram representations, superposition or (reduced) molecular graphs evaluate molecular structure globally. By contrast, local structural features are explored by substructure and pharmacophore searching or QSAR modeling.

Todeschini R, Gramatica P. New 3D molecular descriptors The WHIM theory and QSAR applications. In Kubinyi H, Folkers G, Martin YC, eds. 3D QSAR in Drug Design. Vol. 2. Ligand Protein Interactions and Molecular Similarity. Dordrecht Kluwer/ESCOM, 1998 355-380. 

QSAR is an important technique in ligand-stmeture-based dmg design [19], Potency or toxicity of a set of similar dmgs is correlated with a variety of molecular descriptors with the help of QSAR. Empirical formula is used to rapidly calculate multiple descriptors based on the structure and the connectivity of atoms in the molecule. For example, descriptors such as the molecular weight and the number of H-bond acceptors are easily concluded. Some descriptors, such as logP and molecular polarizability, can be approximated from atomic or group contributions. 

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