3D-QSAR

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]. 

Kubinyi H (Editor) 1993. 3D QSAR in Drug Design, Theory, Methods and Applications. Leiden, ESCOM. 

Pearlman R S and K M Smith 1998. Novel Software Tools for Chemical Diversity. Perspectives in Dn Discovery and Design vols 9/10/ll(3D QSAR in Drug Design Ligand/Protein Interactions ar Molecular Similarity), pp. 339-353. 

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. 

Thibaut U, G Folkers, G Klebe, H Kubinyi, A Merz and D Rognan 1993. Recommendations for CoMF. Studies and 3D QSAR Publications. In Kubinyi H (Editor) 3D QSAR in Drug Design. Leidei ESCOM, pp. 711-728. 

Comparative QSAR is a field currently under development by several groups. Large databases of known QSAR and 3D QSAR results have been compiled. Such a database can be used for more than simply obtaining literature citations. The analysis of multiple results for the same or similar systems can yield a general understanding of the related chemistry as well as providing a good comparison of techniques. 

D QSAR in Drug Design H. Kubinyi, Y. C. Martin, G. Folker, Eds., Kluwer, Norwell MA (1998). (3 volumes)... 

Once a number of lead compounds have been found, computational and laboratory techniques are very successful in rehning the molecular structures to yield greater drug activity and fewer side elfects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side effects. These are the QSAR techniques described in Chapter 30. Recently, 3D QSAR has become very popular for this type of application. These techniques have been very successful in the rehnement of lead compounds. 

PLS (partial least-squares) algorithm used for 3D QSAR calculations PM3 (parameterization method three) a semiempirical method PMF (potential of mean force) a solvation method for molecular dynamics calculations... 

Many different approaches to QSAR have been developed since Hansch s seminal work. These include both two-dimensional (2D) and 3D QSAR methods. The major differences among these methods can be analyzed from two viewpoints (1) the strucmral parameters that are used to characterize molecular identities and (2) the mathematical procedure that is employed to obtain the quantitative relationship between a biological activity and the structural parameters. 

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, e.g., their inability to distinguish stereoisomers. The examples of 3D QSAR include molecular shape analysis (MSA) [34], distance geometry [35,36], and Voronoi techniques [37]. 

H Kubinyi, G FoUcers, YC Martin, eds. 3D QSAR m Drag Design Recent Advances, Vols 2 and 3. Dordrecht, The Netherlands Kluwer, 1998. 

M Baroni, G Costantmo, G Craciam, D Riganelli, R Valigi, S dementi. Generating optimal linear PLS estimations (GOLPE) An advanced chemometnc tool for handling 3D-QSAR problems. Quant Struct-Act Relat 12 9-20, 1993. 

SA DePriest, D Mayer, CB Naylor, GR Marshall. 3D-QSAR of angiotensm-convertmg enzyme and thermolysm inhibitors A comparison of CoMEA models based on deduced and experimentally determined active site geometries. I Am Chem Soc 115 5372-5384, 1993. 

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). 

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. 

Martin YC. Distance comparison (DISCO) A new strategy for examining 3D structure-activity relationships. In Hansch C, Fujita T, editors, Classical and 3D QSAR in agrochemistry. Washington, DC American Chemical Society, 1995. p.318-29. 

Ekins S, Bravi G, Ring BJ, Gillespie TA, Gillespie JS, VandenBranden M, et al. Three dimensional-quantitative structure activity relationship (3D-QSAR) analyses of substrates for CYP2B6. J Pharmacol Exp Ther 1999 288 21-9. 

Medvedev AE, Veselovsky AV, Shvedov VI, Tikhonova OV, Moskvitina TA, Fedotova OA, et al. Inhibition of monoamine oxidase by pirlindole analogues 3D-QSAR and CoMFA analysis. / Chem Inf Comput Sci 1998 38 1137-44. Miller JR, Edmondson DE. Structure-activity relationships in the oxidation of para-substituted benzylamine analogues by recombinant human liver monoamine oxidase A. Biochemistry 1999 38 13670-83. 

Pearlstein RA, Vaz Rl, Kang J, Chen X-L, Preobrazhenskaya M, Shchekotikhin AE et al. Characterisation of HERG Potassium channel inhibition using COMSiA 3D QSAR and homology modeling approaches. Bioorg Med Chem Lett 2003 13 1829-35. 

Livingstone DJ, Manallack DT. Neural networks in 3D QSAR. QSAR Comb Sci 2003 22 510-8. 

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