QSAR with CoMFA

QSAR with CoMFA Tripos 263 http //www.tripos.com/... 

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 results of the 4D-QSAR case study are interesting and provide large amounts of data about the system of interest, and, unlike static 3D-QSAR methods (CoMFA and SOMFA), 4D-QSAR is able to provide the exact locations of statistically important interaction pharmacophore elements. The ability of this method to overcome the question of What conformation to use and predict the bioactive conformation is impressive and a major reason to use the software. Yet it is the ability to construct manifold models and examine several models for the same alignment that is the true benefit of this method. Add to the list the ability to determine the best alignment scheme (based on statistical and experimental results) and this method will provide more information than one could imagine. This abundance of information is key when troubleshooting results that are not in agreement with current beliefs. 

Another approach is based on the combination of molecular interaction fields using the 3D-QSAR technique CoMFA and soft independent modeling of class analogy (SIMCA) [33], Predictions were made for h % ranges by using the data sets from Refs [19, 27], with about 60% correctly classified. 

This is the most popular QSAR approach among the grid-based QSAR techniques. CoMFA compares the molecular potential energy fields of a set of molecules and searches for differences and similarities that can be correlated with differences and similarities in the property values considered [Cramer III, Patterson et al, 1988 Marshall and Cramer III, 1988 Cramer III, DePriest et al, 1993 Folkers, Merz et al, 1993a, 1993b Kim, 1995a Oprea and Waller, 1997 Martin, 1998 Norinder, 1998 Kubinyi, 2003a]. 

Robert D, Amat L, Carbo-Dorca R. Quantum similarity QSAR study of inhibitors binding to thrombin, trypsin and factor Xa, including a comparison with CoMFA and CoMSIA methods. Inti J Quantum Chem 2000 80 265-282. 

QSARs include statistical methods to relate biological activities (most often expressed by logarithms of equipotent molar activities) with structural elements (Free Wilson analysis), physicochemical properties (Hansch analysis), or fields (3D QSAR). The parameters used in a QSAR model are also called (molecular) descriptors. Classical QSAR analyses (Hansch and Free Wilson analyses) consider only 2D structures. Their main field of application is in substituent variation of a common scaffold. 3D-QSAR analysis (CoMFA) has a much broader scope. It starts from 3D structures and correlates biological activities with 3D-property fields (McKinney et al. 2000). 

D-QSAR studies demonstrated that there could be more than one way to fit structure-activity data within a QSAR methodology. A receptor-independent 4D-QSAR study identified the hydrophobic nature of a HIV protease receptor site and helped in structural modification to improve the potency of the AHPBA inhibitors [241], A 4D-fingerprint-based QSAR model developed for AHPBA inhibitors of HIV was generated independent of any receptor structure or alignment information [126]. These models exhibited comparable statistical data with CoMFA, CoMSIA and H-QSAR approaches. This study proved that genuine representation of 3D and conformational properties of compounds is possible using this approach. 

Statistical characteristics of CMF models obtained for these data sets were compared with the same characteristics built for corresponding data sets using the common 3D-QSAR methods, CoMFA (Comparative Molecular Fields Analysis) [18] and CoMSIA (Comparative Molecular Similarity Index Analysis) [24], based on the use of molecular fields. Data on CoMFA and CoMSIA models were taken from Ref. [25]. 

In this chapter, we have provided a critical view of the 3D-QSAR arena, some practical steps for modeling with CoMFA, and a set of criteria for assessing model validity. The need for quantitative models stems from the difficulty in discerning simple, intuitive (qualitative) structure-activity relationships. Although QSAR provides a rational framework for testing hypotheses, the QSAR models remain oversimplifications of the modeled process, and as such, are incomplete. The ultimate utility of any model rests with the scientist Is the model better than having no model at all ... 

Use of other molecular descriptors (alone or in combination) for deriving QSAR models is also common in environmental chemistiy. However, it is interesting to note that only two-dimensional descriptors are employed (Figure 4). Indeed, the number of QSAR models designed with three-dimensional descriptors is very scarce in environmental sciences. Thus, for example, in the whole environmental QSAR literature, fewer than 10 publications deal with CoMFA (e.g., Briens and co-workers, Dearden and Stott ) while this approach is now widely used in drug design (see Comparative Molecular Field Aiuilysis (CoMFA)). 

The ability of partial least squares to cope with data sets containing very many x values is considered by its proponents to make it particularly suited to modern-day problems, where it is very easy to compute an extremely large number of descriptors for each compound (as in CoMFA). This contrasts with the traditional situation in QSAR, where it could be time-consuming to measure the required properties or where the analysis was restricted to traditional substituent constants. 

The biggest limitation of the CoMFA method is the alignment step. The algorithm superimposes the portions of the inhibitors that are of similar stmcture, assuming that they bind with similar orientations in the active site of the enzyme, which is not necessarily the case. Also, because of a problem with alignment, a CoMFA may fail when a few molecules are very dissimilar from all others in the series. Like QSAR, CoMFA does not require a stmcture of the relevant biological receptor, but does require knowledge about a series of inhibitory compounds. 

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. 

A widely used 3-D QSAR method that makes use of PLS is comparative molecular field analysis (CoMFA), in which a probe atom is used to calculate the steric and electronic fields at numerous points in a 3D lattice within which the molecules have been aligned. Poso et al. [56] used the technique to model the binding of coumarins to cytochrome P450 2A5, with similar results to those obtained by Bravi and Wikel [55]. Shi et al. [57] used it to model the estrogen receptor binding of a large diverse set of compounds, and Cavalli et al. [58] used it to develop a pharmacophore for hERG potassium... 

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