3D-QSAR methods

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. 

Gaillard, P., Carrupt, P. A., Testa, B Boudon, A. Molecular lipophilicity potential, a tool in 3D QSAR Method and applications, f Comput.-Aided Mol. Des. 1994, 8, 83-96. 

Partial least squares (PLS) projections to latent structures [40] is a multivariate data analysis tool that has gained much attention during past decade, especially after introduction of the 3D-QSAR method CoMFA [41]. PLS is a projection technique that uses latent variables (linear combinations of the original variables) to construct multidimensional projections while focusing on explaining as much as possible of the information in the dependent variable (in this case intestinal absorption) and not among the descriptors used to describe the compounds under investigation (the independent variables). PLS differs from MLR in a number of ways (apart from point 1 in Section 16.5.1) ... 

Coats, E.A. The CoM FA steroids as a benchmark dataset for development of 3D-QSAR methods. In 3D QSAR in Drug Design, Vol. 3, Recent Advances, Kubinyi, H., Folkers, G., and Martin, Y.C. (Eds). Kluwer/ESCOM, Dordrecht, Netherlands, 1998, 199-213. 

The use of receptor-dependent (RD) QSAR adds to the QSAR model through the inclusion of ligand-receptor interactions. 5D-QSAR is unique because it mimics the binding site of the receptor (constructed from experimental data or random placement of physicochemical properties) to aid in the construction of an optimal QSAR model and to aid in the construction of a pharmacophore, yet is also alignment dependent. The FEFF 3D-QSAR method is a true RD-QSAR method using the solved 3D structure of the receptor in the calculation of ligand-receptor interaction values. 

Constructing the model using all the calculated descriptors is feasible, but this leads to overgeneralized models. The problem of many descriptors is further compounded when using 3D and /zD-QSAR methodology where thousands of descriptors are calculated. The end results of calculating all possible descriptors for traditional QSAR and the data returned from 3D-QSAR methods are tables containing many columns (descriptors) and few rows (molecules, bioactivities). 

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. 

Topliss Decision Tree Method. This method is quicker and easier to use than the Hansch method. The Topliss scheme is an empirical method in which each compound is tested before an analog is planned, and is compared in terms of its physical properties with analogs already planned. Like the Free-Wilson method, the Topliss decision tree is no longer extensively used. The 2D- and 3D-QSAR methods are gradually supplanting the ID methods. 

This chapter focuses on computational studies which employ a combination of structure-based and 3D QSAR methods as a mean to predict the affinity of a ligand for its receptor. The comprehensive utility of this approach is exemplified by case studies published in the last few years and from our laboratory. Special emphasis will be placed on a detailed description of the combined structure-ligand-based approach and the successful application of this procedure to the design of novel drug molecules. 

Sanz, F. 3D QSAR Methods on the Basis of Ligand-Receptor Complexes. Application of Combine and GRID/GOLPE Methodologies to a Series of CYP1A2 Inhibitors. /. Comput.-Aided Mol. Des. 2000, 13, 341-353. 

Tervo, A. J., Nyroenen, T. H., Ronkko, T., Poso, A. A Structure-Activity Relationship Study of Catechol-O-methyltrans-ferase Inhibitors Combining Molecular Docking and 3D QSAR Methods. 

D-QSAR. Since compounds are active in three dimensions and their shape and surface properties are major determinants of their activity, the attractiveness of 3D-QSAR methods is intuitively clear. Here conformations of active molecules must be generated and their features captured by use of conformation-dependent descriptors. Despite its conceptual attractiveness, 3D-QSAR faces two major challenges. First, since bioactive conformations are in many cases not known from experiment, they must be predicted. This is often done by systematic conformational analysis and identification of preferred low energy conformations, which presents one of the major uncertainties in 3D-QSAR analysis. In fact, to date there is no computational method available to reliably and routinely predict bioactive molecular conformations. Thus, conformational analysis often only generates a crude approximation of active conformations. In order to at least partly compensate for these difficulties, information from active sites in target proteins is taken into account, if available (receptor-dependent QSAR). Second, once conformations are modeled, they must be correctly aligned in three dimensions, which is another major source of errors in the system set-up for 3D-QSAR studies. 

Recent developments include 3D QSAR methods which relate regions of the binding site with complementary properties1471. The conformation of each molecule then needs be computed and the descriptor property determined. Another interesting development is the Electron Topological (ET) approach in QSAR meth-ods[48]. Molecular compounds are described by quadratic matrices (n2, n number of atoms), where elements close to the diagonal represent electronic parameters while the other elements are related to the structure. 

Polanski J, Gieleciak R, Bak A (2002) The comparative molecular surface analysis (COMSA) - a nongrid 3D QSAR method by a coupled neural network and PLS system Predicting pKa values for benzoic and alkanoic acids. J Chem Inf Comput Sci 42 184-191... 

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. 

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