3D-QSAR models

Hopfinger et al. [53, 54] have constructed 3D-QSAR models with the 4D-QSAR analysis formahsm. This formalism allows both conformational flexibility and freedom of alignment by ensemble averaging, i.e., the fourth dimension is the dimension of ensemble sampling. The 4D-QSAR analysis can be seen as the evolution of Molecular Shape Analysis [55, 56]. 

Quantitative Structure-Activity Relationship studies search for a relationship between the activity/toxicity of chemicals and the numerical representation of their structure and/or features. The overall task is not easy. For instance, several environmental properties are relatively easy to model, but some toxicity endpoints are quite difficult, because the toxicity is the result of many processes, involving different mechanisms. Toxicity data are also affected by experimental errors and their availability is limited because experiments are expensive. A 3D-QSAR model reflects the characteristics of... 

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Figure 5.1 Superimposition of the pharmacophoric schemes derived from the 3D QSAR models published by Ekins et al. [14] (vertical lines filling), Cavalli etal. [15] (horizontal lines filling) and Pearlstein etal. [17] (dotfilling). The central black circle represents the...

Indeed, considering the latter 3D QSAR model, the features that make a molecule suitable to bind to the hERG channel start delineating in a chemically interpretable manner, but, it is rather dear how these kinds of models emphasize mostly the 3D steric aspects of molecules, depending mainly on factors such as the conformation (or the conformational analysis protocol) or the alignment of the molecules. To obtain a description of the characteristics of hERG-blocking molecules in terms of measurable (computable) properties in a way that the physicochemical determinants of the activity can be identified, the classical 2D QSAR approach is well suited. 

Oprea, T.l. 3D-QSAR modeling in dmg design. In Computational Medicinal Chemistry and Drug Discovery,... 

Balls, T., Andersen, K., Soby, K.K., and Liljefors, T. al Adrenoceptor subtype selectivity 3D-QSAR models for a new class of al adrenoceptor antagonists derived from the novel antipsychotic sertindole./. Mol. Graph. Mod. 2003, 21, 523-534. 

Oprea, T.I. 3D-QSAR modeling in dmg design. In Computational Medicinal Chemistry and Drug Discovery, Tollenaere, J., De winter, H., Langenaeker, W., Bultinck, P. (Eds). Marcel Dekker, New York, 2004, 571-616. 

The published QSAR [59-61] and 3D-QSAR [62-65] models for HDAC inhibitors were used to explain the differences in activity of hydroxamate-based compounds. All the reported models, which showed moderate to good internal predictivity, were mainly used in a retrospectively way to explain the biological activities of H DAC inhibitors. Generally, the 3D-QSAR models were compared with ligand docking results to get insight into the structural requirements for anti-HDAC activity. 

The focus of 3D-QSAR is to identify and quantitatively characterize the interactions between the ligand and the receptor s active site. As the title of the field suggests, the main basis of the QSAR models are the molecules 3D atomic (Cartesian) coordinates. The interactions between the atomic 3D coordinates and the receptor are correlated to the bioactivities producing a 3D-QSAR model. There are several methods to achieve the creation of QSAR... 

Hopfinger et al. (5) developed 4D-QSAR analysis which incorporates conformational and alignment freedom into the development of a 3D-QSAR model and can be viewed as the evolution of molecular shape analysis, also developed by Hopfinger (26,102) in the early 1980s. 

Fig. 9. Spinning. Four A yV-dimethyl-a-bromophenethylamines ligands were aligned by their ethylamines (tail) as denoted in Alignment 7 of Table 5. Alignments that do not take into consideration the substituted regions of interest can lead to poor alignments providing dis-informative 3D-QSAR models. It is for this reason that many alignment schemes are tested to elucidate the one that will render the most useful model.

The major hurdle to overcome in the development of 3D-QSAR models using steric, electrostatic, or lipophilic fields is related to both conformation selection and subsequent suitable overlay (alignment) of compounds. Therefore, it is of some interest to provide a conformation-ally sensitive lipophilicity descriptor that is alignment-independent. In this chapter we describe the derivation and parametrization of a new descriptor called 3D-LogP and demonstrate both its conformational sensitivity and its effectiveness in QSAR analysis. The 3D-LogP descriptor provides such a representation in the form of a rapidly computable description of the local lipophilicity at points on a user-defined molecular surface. 

Ekins et al. (201) used the MS-WHIM descriptors to construct 3D and 4D QSAR models for the log(l/Aj) of 14 competitive inhibitors of CYP3A. The 3D QSAR of the CYP3A4-mediated midazolam l -hydroxylation was shown to be predictive yielding a leave-one-out (LOO) q2 value of 0.32. Although the 4D QSAR methodology includes conformational changes, it did not provide for a significant improvement over the 3D QSAR (LOO q2 0.44). Two other datasets (242,243) were used to create 3D and 4D QSAR models. In both datasets, it was not possible to build predictive 3D QSAR models however, 4D QSAR models were constructed (LOO q2 = 0.41-0.56). 

Wang (247) and coworker s 3D QSAR model for 31 dillapiol revealed that the activity was correlated with the steric bulk of the substitutes in position 5 and 6 and with the electron density of the groups at position 6. Unfortunately, no more information about that work has been found. 

Huang, X. et al., 3D-QSAR model of flavonoids binding at benzodiazepine site in GABAa receptors, J. Med. Chem., 44, 1883, 2001. 

Many 2D or 3D QSAR models rely on multiple structural and/or property attributes to build a quantitative model, and this makes them difficult to interpret qualitatively as design guidelines. 

A 3D-QSAR model for artemisinin pharmacophore has been developed. 

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