CoMFA application

Cavalli A, Greco G, Novellino E, Recanatini M. Linking CoMFA and protein homology models of enzyme-inhibitor interactions an application to nonsteroidal aromatase inhibitors. Bioorg Med Chem 2000 8 2771-80. 

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 method used to align molecules is still an area of contention. Applications like MOE-FlexAlign (30,39) and FlexS (37) are used to help discover different alignment configurations or to ensure that user bias has not entered the alignment scheme. Methods like MOE-FlexAlign and CoMFA s Field Fit (4) are useful if one knows the bioactive conformation or if the molecules of inter-... 

To test the applicability of this approach, eight compounds were randomly removed. The remaining 52 compounds were used to develop a CoMFA model. The resultant model was then used to predict the solubility of the removed compounds. Results (Table 3.7) show that predicted values agree well with the experimental values. 

Folkers, G., Merz, A., Rognan, D. CoMFA Scope and Limitations. In 3D QSAR in Drug Design. Theory, Methods and Applications, Kubinyi, H. (ed.), ESCOM Science Publishers, Leiden, 1993. 

Kim, K.H., Greco, G., Novellino, E.A Critical Review of Recent CoMFA Applications. Perspect. Drug Discov. Des. 1998, 72, 257-315. 

Molecules are characterized by potential hydrogen bonding, polar, hydrophobic, and electrostatic interactions in 3D space, using 3D molecular fields. Techniques such as Comparative Molecular Field Analysis (CoMFA), which considers the 3D distribution of electrostatic and steric fields, have been applied to congeneric series of enzyme substrates or inhibitors generating 3D QSAR equations. Most examples of such applications are to modeling CYP substrate and inhibitor specificity and these have been extensively reviewed in the literature (Ekins et al., 2000 2001 Ter Laak and Vermeulen, 2001 Ter Laak et al., 2002). 

A survey on 3D-QSAR literature (Oprea 2004) reported more than 1100 entries in the Chemical Abstracts database on CoMFA, 3D-QSAR, and related keywords. For CoMFA alone, 586 publications between 1988 and 2001 demonstrate its wide distribution and applicability. As the number of potential targets in drug discovery is steadily increasing, it is likely that 3D-QSAR models and methodologies will continue to be developed in the future. Successful applications were not only reported to understand target related affinity but also for some ADME relevant targets... 

Beside the descriptors, further attempts have been made to encode the 3D molecular structures with functions. Such are 3D-MoRSE code [54] spectrum-like representations [55] and radial distribution functions [56]. Also, experimentally determined infrared, mass, or NMR spectra can be taken to represent a molecule [57]. Another example is comparative molecular field analysis (CoMFA) where the molecular 3D structures are optimized together with the receptor [58]. This approach is often applied in drug design or in specific toxicology studies where the receptor is known. The field of molecular descriptors and molecular representations has exploded in the recent decades. Over 200 programs for calculating descriptors and different QSAR applications are listed on web page [59]. 

The use of the Pathfinder descriptor for QSAR is exemplified here through application to two datasets taken from the literature (i) 31 steroids utilized in the original CoMFA study [3], since considered a benchmark for evaluating QSAR methods [4] (ii) 55 inhibitors of HIV-1 reverse transcriptase proposed by Chan and coworkers [5], interestingly this series could only be successfully correlated by means of a structure-based approach [6]. 

An inexperienced user or sometimes even an avid practitioner of QSAR could be easily con-fiased by the multitude of methodologies and naming conventions used in QSAR studies. Two-dimensional (2D) and three-dimensional (3D) QSAR, variable selection and artificial neural network methods, comparative molecular field analysis (CoMFA), and binary QSAR present examples of various terms that may appear to describe totally independent approaches, which cannot be even compared to each other. In fact, any QSAR method can be generally defined as the application of mathematical and statistical methods to the problem of finding empirical relationships (QSARmod-els)of the form, D . D ), where... 

Perhaps the most popular example of 3D-QSARis the comparative molecular field analysis (CoMFA), developed by Cramer et al. (40), which has elegantly combined the power of 3D molecular modeling and partial least-square (PLS) optimization technique (41, 42) and found wide applications in medicinal chemistry and toxicity analysis (see below). Most of... 

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