H-bond donors and the sum of H E-state indices for all nonpolar hydrogen atoms in a molecule. These four topological indices highlight the structural features that determine the potency of these inhibitors, i.e. the molecular globularity, the skeletal branching, the H-bond-donating ability and the presence of nonpolar groups. The QSAR model was validated through the prediction of 15 compounds from an external test set, yielding a mean absolute error MAE of 0.82. The QSAR model has a direct structural interpretation that facilitates the design of better HIV-1 protease inhibitors. [Pg.94]

The plCso values predicted for a test set of six compounds have a good correlation with the experimental values, r = 0.706, indicating that the QSAR model is stable and reliable. [Pg.95]

The steady increase in the frequency of tuberculosis infections resistant to conventional drug therapy highlights the need for new drugs that are efficient against Mycohacterium tuberculosis infections. Experimental studies showed that some quinolone derivatives are efficient antibacterials for M. tuberculosis as well as other mycobacterial infections, such as those with M.fortuitum and M. smegmatis. Bagchi et al. used a dataset of 68 quinolone derivatives 5 to model their MIC against M. fortuitum and M. smegmatis with ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) [32]. The QSAR models were developed from a pool of247 topological indices computed with Polly and Molconn-Z, and included the entire spectrum of E-state indices. The best LOO predictions for M. fortuitum MIC were obtained with ridge regression, i.e. r =0.900 and q =0.796 for RR, q =0.566 for PCR, and q =0.792 for PLS, whereas the best predictions for M. smegmatis MIC were obtained with partial least squares, i.e. r =0.967 and q =0.849 for RR, q =0.595 for PCR, and q =0.854 for PLS. The E-state descriptors used in combination with other topological indices are effective in modeling the MIC of quinolone derivatives against M. fortuitum and M. smegmatis. [Pg.95]

The structural features that determine the selectivity for cyclooxygenase (COX)COX-2 versus COX-1 binding affinity to l-(substituted phenyl)-2-(4-aminosulfonyl/ methylsulfonyl)-substituted benzenes 6 was investigated by Chakraborty et al. with QSAR models based on E-state indices and indicator variables [33], The electrotopological indices represented atomic E-state values for atoms from the common skeleton and sums of E-state indices for groups of atoms. Significant QSAR models were obtained for all three properties investigated, i.e. r =0.815 and 3loo = 0-675 for plC5o(COX-l), r =0.887 and qiLoo = 0.842 for plC5o(COX-2), and H = 0.746 and = 0.601 for [plC5o(COX-2)-plCs (COX-l)]. [Pg.96]

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]. [Pg.429]

The information in the structures and known activity data is good enough to create a QSAR model with a confidence of 75% [Pg.231]

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. [Pg.313]

Shen M, Beguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds. J Med Chem 2004 47(9) 2356-64. [Pg.317]

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003 43(6) 1947-58. [Pg.318]

MetaDrug Metabolism database. Metabolite prediction. Metabolite prioritization, QSAR models for enzymes, transporters and network building algorithms for Systems-ADME/Tox www.genego.com [Pg.448]

Livingstone [24] has given a number of recommendations for successful QSAR modeling [Pg.474]

MLR is the most widely used of the QSAR modeling techniques. Walker et al. [15] have published guidelines for the development and use of MLR-based QSARs, and Cronin and Schultz [41] have discussed their potential pitfalls. [Pg.477]

A QSAR for which the standard error of each descriptor is given concerns the bradycardic effect of a series of tetraalkylbispidines [47]. The QSAR models the selectivity between the desired bradycardic effect and the adverse contractile effect. It is important, in assessing and modeling drug toxicity, that the toxic effect is assessed relative to the desired effect as described above. The QSAR developed for the selectivity of the tetraalkylbispidines was [Pg.478]

Dearden JC, Netzeva TI. QSAR modelling of hERG potassium channel inhibition with low-dimensional descriptors. I Pharm Pharmacol 2004 56 Suppl S-82. [Pg.490]

Devillers J. A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus. SAR QSAR Environ Res 2001 11 397-417. [Pg.491]

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