3D-QSAR CoMFA

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). 

Various endeavors have been undertaken to get insight into the 3D selector-selectand complex structures and to elucidate chiral recognition mechanisms of cinchonan carbamate selectors for a few model selectands (in particular, DNB-Leu). Such studies comprised NMR [92-94], ET-IR [94-96], X-ray diffraction [33,59,92,94], and molecular modeling investigations (the latter focusing on molecular dynamics [92,93,97], and 3D-QSAR CoMFA studies [98]). 

Moron, J.A., Campillo, M., Perez, V., Unzeta, M., Pardo, L. Molecular determinants of MAO selectivity in a series of indolylmethylamine derivatives biological activities, 3D-QSAR/CoMFA analysis, and computational simulation of ligand recognition./. Med. Chem. 2000, 43, 1684-1691. 

Thaimattam, R., Daga, P., Rajjak, S.A., Banerjee, R., Iqbal, J. 3D QSAR CoMFA, CoMSIA Studies on Substituted Ureas as Raf-1 Kinase Inhibitors and its Confirmation with Stmcture-based Studies. Bioorg. Med. Chem. 2004, 12, 6415-6425. 

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.

In Sect. 2.1, we have presented the 3D-QSAR CoMFA studies on a series of indole/benzoimidazole-5-carboxamidines as FXa inhibitors. Herein we present the 3D-QSAR/CoMFA models developed for the same series of indole/benzoimidazole-5-carboxamidines [69] as thrombin inhibitors, using 29 training set molecules and validated with seven test set molecules (Fig. 5). 

Belvisi, L., Bravi, G, Catalano, G., Mabilia, M., Salimbeni, A. and Scolastico, C. (1996). A 3D QSAR CoMFA Study of Non-Peptide Angiotensin II Receptor Antagonists. J.Comput.Aid. Molec.Des., 10, 567-582. 

Lopez-Rodriguez, M.L., Murda, M., Benhamu, B., Viso, A., Campdlo, M. and Pardo, L. (2002) Benzimidazole derivatives. 3. 3D-QSAR/CoMFA model and computational simulation for the recognition of 5-HT4 receptor antagonists. J. Med. Chem., 45, 4806 815. 

D-QSAR (CoMFA, Catalyst, GRID/GOLPE,...) substrate behavior, relative rates, inhibitor behavior,... ... 

Note PRESS and q may relate to any property that is being modelled and not just activity , will always be smaller than r. When q > 0.3, a model is considered significant. Although cross-validation may seem a robust validation technique, some difficulties should not be overlooked. Variables that do not contribute to prediction, i.e. cause noise in the model, may have detrimental effects on CV. This may particularly play a role when many variables have to be considered, such as in a 3D-QSAR CoMFA analysis (see Chapter 25). A procedure for variable selection in the case of many variables has been developed and is named GOLPE (generating optimal linear PLS estimations). ... 

Avram et al. [118] used 3D-QSAR CoMFA techniques to study a dataset of symmetric and non-symmetric cyclic urea HIVPl. The anti-HIVPR inhibitory activity data was taken from the literature [72,77]. The electrostatic and steric fields were calculated by default settings with sp C-atom probe with a + 1 charge. The regression models derived with the PLS and LOO cross vahdation technique were developed. The best model with a correlation coefficient (r ) of 0.981 and cross-validated correlation (q ) of 0.525 showed a higher contribution from steric fields (58.6%) compared to electrostatic fields (41.1%). Two additional models were reported with q = 0.627 and 0.536, respectively. All the models were improved further by omitting outliers. 

Ungwitayatorn et al. [240] reported the 3D-QSAR CoMFA/CoMSIA studies for a series of 30 Chromone derivatives of HIVPI. The dataset was divided into a training/test set of 30/5 compounds based on the distribution of biological activity and the variety of substitution pattern. Superposition and field fit alignment criteria were used for model development in CoMFA. The best predictive CoMFA model with steric (46%) and electrostatic (54%) fields gave cross-validated of 0.763 and non-cross-validated of 0.967 with a standard error of estimate (S) of 5.092. The PLS protocol and stepwise procedure were... 

Molecular Determinants of MAO Selectivity in a Series of Indolylmethylamine Derivatives Biological Activities, 3D-QSAR/CoMFA Analysis, and Computational Simulation of Ligand Recognition. 

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