A 3D QSAR

Rhyn K-B, H C Patel and A J Hopfinger 1995. A 3D-QSAR Study of Anticoccidal Triazlnes Usir Molecular Shape Analysis. Journal of Chemical Information and Computer Science 35 771-778. 

Imidazolinyl) derivative of 8-methyl-2,3,6,7-tetrahydro-5 f- and 8-methyl-7-methoxy-5-oxo-2,3-dihydro-5 f-pyrido[l, 2, i-de]-1,4-benzoxazines were included in a 3D-QSAR CoMFA study on imidazolinergic I2 ligands (00JMC1109). 

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... 

A 3D-QSAR analysis of in vitro binding affinity and selectivity of 3-izoxazolyl-sulfonylaminothiophenes as endothelin receptor antagonists. Quant. Struct.-Act. Relot. 1999, 18, 124-133. 

The popularity of commercial programs such as Comparative Molecular Field Analysis (4,12) (CoMFA) and Catalyst (13) has limited both the evaluation and use of other QSAR methodologies. Often well-known issues associated with CoMFA and Catalyst have come to be viewed as shortcomings that simply are accepted as working limitations in a 3D-QSAR analysis. In this section we challenge this position and present 3D- and nD-QSAR methods that are able to overcome some of the issues associated with current mainstream 3D-QSAR application products. 

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. 

Pastor, M., Cmciani, G., and Watson, K. (1997) A strategy for the incorporation of water molecules present in a ligand binding site into a 3D QSAR analysis.. /. Med. Chem. 40, 4089-4102. 

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

Furthermore the docking poses were used as an alignment tool for the generation of a 3D-QSAR model which was able to quantitative explain the different affinity of the ligands. 

Norinder U., Gustavsson A.-L. and Liljefors T. (1997) A 3D-QSAR study of analogs of (Z)-5-decenyl acetate, a pheromone component of the turnip moth, Agrotis segetum. J. Chem. Ecol. 23, 2917-2934. 

Interesting information in this context was also provided by the work of Feher and Schmidt, who proposed two possible alignments between CA4 and other important CSI, including colchicine [33], The represented a test for the newly developed MultiSEAL procedure, which allows the application of the steric and electrostatic alignment (SEAL) [34] method to multiple conformations of multiple molecules. It should be emphasized that, when robust, an alignment method can be profitably used not only to prepare compounds for a 3D-QSAR analysis, but also to identify and locate potential pharmacophoric groups in a set of structurally diverse molecules. The CSI used by Feher and Schmidt were colchicine, CA4, allocolchicine (6), 2-methoxy-5-(2, 3, 4 -trimethoxyphenyl)-tropone (MTC) (7) and 2-methoxyestradiol (8) (Chart 5). 

At the end of the equilibration protocol, a 3D-QSAR equation with a good correlation (r = 0.81) and a low root mean square deviation (rmsd 0.85) on the estimated interaction energies were derived. The predictive power of the model was evaluated with a test set of 11 taxanes and epothilones, obtaining a prediction coefficient of 0.78. 

Figure 13.11 Overview diagram of the NCTR Four-Phase approach for priority setting. In Phase I, chemicals with molecular weight < 94 or > 1000 or containing no ring structure will be rejected. In Phase II, three approaches (structural alerts, pharmacophores, and classification methods) that include a total of 11 models are used to make a qualitative activity prediction. In Phase III, a 3D QSAR/CoMFA model is used to make a more accurate quantitative activity prediction. In Phase IV, an expert system is expected to make a decision on priority setting based on a set of rules. Different phases are hierarchical different methods within each phase are complementary.

Wiese, T.E., Polin, L.A., Palomino, E., and Brooks, S.C., Induction of the estrogen specific mitogenic response of MCF-7 cells by selected analogues of estradiol-17 beta A 3D QSAR study, J. Med Chem., 40, 3659-3669, 1997. 

The work described by Deadman et al. [100] considered a subset of the above set of thrombin inhibitors. A training set of 16 homologous nonpeptide inhibitors whose conformations had been generated in continuum solvent (MacroModel) and clustered into conformational families (XCluster) was regressed against this pharmacophore so as to obtain a 3D-QSAR mode. 

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