Similarity QSAR analyses

A Brief Review of the QSAR Technique. Most of the 2D QSAR methods employ graph theoretic indices to characterize molecular structures, which have been extensively studied by Radic, Kier, and Hall [see 23]. Although these structural indices represent different aspects of the molecular structures, their physicochemical meaning is unclear. The successful applications of these topological indices combined with MLR analysis have been summarized recently. Similarly, the ADAPT system employs topological indices as well as other structural parameters (e.g., steric and quantum mechanical parameters) coupled with MLR method for QSAR analysis [24]. It has been extensively applied to QSAR/QSPR studies in analytical chemistry, toxicity analysis, and other biological activity prediction. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least-squares (PLS) analysis has been employed [25]. 

Cocchi, M. and De Benedetti, P.G. (1998) Use of the supermolecule approach to derive molecular similarity descriptors for QSAR analysis. Journal of Molecular Modelling, 4, 113-131. 

Hammett correlations were developed from experimental data for substituted phenols studied under the UV/Ti02 process (D Oliviera et al., 1993). The mechanism for this reaction is understood to proceed via the hydroxyl radical. Experimental data from the study of dichlorophenols and trichlo-rophenols under UV/Ti02 were used for QSAR analysis (D Oliviera et al., 1993). Figure 9.13 demonstrates the QSAR model for substituted phenols formulated from experimental data. The QSAR model developed for substituted phenols shows a goodness of fit of 0.9766. A good correlation was also established for substituted phenols using Hammett s constant, o the correlation coefficient is 0.987 (D Oliviera et al., 1993). Similar correlation coefficients for the constants o and ores demonstrate that the descriptor ores can be used to accurately predict kinetic rate constants for substituted phenols. 

Duca, S., Hopfmger, A. J. Estimation of Molecular Similarity Based on 4D-QSAR Analysis Formalism and Validation. 

Bringmann G, Rummey C (2003) 3D QSAR investigations on antimalarial naphthylisoqui-noline alkaloids by comparative molecular similarity indices analysis (CoMSIA), based on different alignment approaches. J Chem Inf Comput Sci 43(1) 304-316... 

Also important to the validation process of QSARs is vertical validation. In this instance, quantitatively similar QSARs are developed with similar descriptors but using data for a different toxic endpoint. For example, the investigation of Karabunarliev et al. (1996b) modeled acute aquatic toxicity data for the fathead minnow Pimephales promelas. The compounds considered in the analysis were confined to substituted benzenes, and descriptors limited to log Kow and Amjx. The fish toxicity QSAR (log [LQ,]-1 = 0.62 log K, + 9.17 A - 3.21 n = 122 R2 = 0.83 i = 0.16 F = 292) of Karabunarliev et al. (1996b) was very similar in terms of slope, intercept, and statistical fit to the QSAR presented in Equation 12.2. The fact that different endpoints provide very similar QSARs indicates that the QSAR is valid across protocols. This shows the universality of the model. 

The next step was made by Klebe et al. [50]. Two 3D-QSAR methods were applied to get three-dimensional quantitative structure-activity relationships using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities toward Factor Xa to yield statistically reliable models of good predictive power [51-54] the widely used CoMFA method (for steric and electrostatic properties) and the comparative molecular similarity index analysis (CoMSlA) method (for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties). These methods allowed the consideration of various physicochemical properties, and the resulting contribution maps could be intuitively interpreted. 

Tel. 218-720-4279, fax 218-720-4219, e-mail sbasak ua.d.umn.edu Generation of connectivity and other molecular descriptors for use in QSAR and similarity/dissimilarity analysis. Silicon Graphics and PCs. 

Comparative Molecular Similarity Indices Analysis, among the grid-based QSAR techniques, implements the steric, electrostatic, hydrophobic, and hydrogen-bonding - similarity indices utilized in the molecular alignment program SEAL [Klebe et al., 1994a Klebe, 1998 Klebe and Abraham, 1999],... 

Quantitative Infonnation Analysis is the term proposed by Kier to denote structure/response correlations, where the word analysis is chosen to avoid any restriction to QSAR/QSPR models, but naturally includes similarity/diversity analysis as well as any explorative analysis or model which refers not only to relationships with the molecular structure. 

Klebe, G. (1998). Comparative Molecular Similarity Indices Analysis CoMSIA. In 3D QSAR in Drug Design - Vol. 3 (Kubinyi, H., Folkers, G. and Martin, Y.C., eds.), Kluwer/ESCOM, Dordrecht (The Netherlands), pp. 87-104. 

Comparative Molecular Similarity Indices Analysis grid-based QSAR techniques > Comparative Molecular Surface Analysis grid-based QSAR techniques > Comparative Receptor Surface Analysis = CoRSA > Comparative Spectral Analysis spectra descriptors... 

CoMSIA= Comparative Molecular Similarity Indices Analysis grid-based QSAR techniques... 

Finally, the stereodynamic representation of a molecule is a time-dependent representation, which adds structural properties to the 3D representations, such as fiexibility, conformational behavior, transport properties, and so on. Dynamic QSAR, 4D-Molecular Similarity Analysis, and 4D-QSAR Analysis are examples of a multiconformational approach. 

Other common descriptors derived from substructure-based methods are discussed below. Among these, hash structural codes, structural keys, and fingerprints are mostly applied in virtual screening and substructure searching, whereas pharmacophore-based descriptors are more successful in similarity/diversity analysis and QSAR/QSPR studies. 

To generate the molecular descriptors, by which molecular similarity is evaluated, first, the Conformation Energy Profile (CEP) of each molecule is estimated. This is indicated by the Boltzmann distribution plot of the number of conformers N(AE) at energy AE. CEP was first defined and used in the framework of 4D-QSAR Analysis. 

In 4D-Relative Molecular Similarity Analysis, to construct the MDDM for each pair of IPEs, the Grid Cdl Occupancy Descriptors (GCODs) need first to be calculated by performing a partial 4D-QSAR analysis. A GCOD is defined as the probability that a given IPE type will occupy a specific grid cell in a given molecule. 

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