4D-QSAR

Before the comparative molecular field analysis (CoMFA), BCUT descriptors, 4D-QSAR, and HYBOT descriptors arc discussed in more detail, some further descriptors are listed briefly. 

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]. 

In 4D-QSAR, a grid is used to determine the regions in 3D space responsible for binding. Nevertheless, neither a probe nor interaction energy is used. 

Ekins S, Bravi G, Binkley S, Gillespie JS, Ring BJ, Wikel JH, et al. Three and four dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2D6 inhibitors. Pharmacogenetics 1999 9 477-89. 

The concept of property space is progressively being used to gain a deeper understanding of the dynamic behavior of a single compound in different media (as we illustrate below with acetylcholine, see Section 1.4.2) or bound to biological targets (the carnosine-carnosinase complex, see Section 1.4.3), but it can be used also with a set of compounds to derive fertile descriptors for dynamic QSAR analyses (4D QSAR, see Section 1.4.4). 

Vedani, a., Mcmasters, D.R., and Dobler, M. Multi-conformational ligand representation in 4D-QSAR Reducing the bias associated with ligand alignment. Quant. Struct.-Act. Relat. 2000, 19, 149-161. 

Key Words 2D-QSAR traditional QSAR 3D-QSAR nD-QSAR 4D-QSAR receptor-independent QSAR receptor-dependent QSAR high throughput screening alignment conformation chemometrics principal components analysis partial least squares artificial neural networks support vector machines Binary-QSAR selecting QSAR descriptors. 

The methodology of nD-QSAR adds to the 3D-QSAR methodology by incorporating unique physical characteristics, or a set of characteristics, to the descriptor pool available for the creation of the models. The methods of Eigenvalue Analysis (40) (EVA) and 4D-QSAR (5) are examples of using unique physical characteristics in the creation of a QSAR model. 4D-QSAR uses an ensemble of molecular conformations to aid in the creation of a QSAR. The EVA-QSAR method uses infrared spectra to extract descriptors for the creation of the QSAR model. 

Fig. 3. Examples of three-point alignment schemes. (A) The core of the molecules used in the case study with alignment scheme denoted as used for the 4D-QSAR portion. There are three distinct regions for alignment (head =, middle = , tail = ). In (A) the scaffold (analog) has distinct head, middle, and tail regions making the division of the molecule simple. The molecule in B is considerably more complex (due to its symmetry) to divide into three sections. The molecule is similarly in size to the molecule in A, yet is divided into three overlapping sections for alignment (head =, middle = , tail = ).

Hopfinger et al. (5) developed 4D-QSAR analysis which incorporates conformational and alignment freedom into the development of a 3D-QSAR model and can be viewed as the evolution of molecular shape analysis, also developed by Hopfinger (26,102) in the early 1980s. 

There are two possible application strategies for the use of 4D-QSAR models as a VHTS. The first is to take a collection of (manifold) 4D-QSAR models and create a consensus 4D-QSAR model. The consensus model is evaluated for each molecule using all of the individual 4D-QSAR models ... 

Another way to form a VHTS from several 4D-QSAR models is to use all the distinct grid cell occupancy descriptors (GCODs) and the bioactivity (AG) values of the training set. This simple method of constructing a VHTS-QSAR model is likely to suffer from overfitting the data, but is useful in a VHTS... 

W. Graham Richards (11) group. Using 4D-QSAR (5) the role of the conformation in creating a QSAR model and extract the bioactive conformation was explored. This case study is intended to be a comparison between the different... 

The results of the 4D-QSAR study are the most interesting due to the proposed steric and interaction-based descriptors. One of the main differences between this method and the others examined is the use of multiple alignment schemes to determine the best alignment based on Q2 values and the utility of the models produced. The alignments explored in this case study are displayed in Table 5. 

The use of manifold models as an aid to gauge the important descriptors of a 4D-QSAR model is an intriguing proposition. Methods such as CoMFA and SOMFA provide the user with a single, graphical QSAR model from which to harvest usable information. Traditional QSAR results are better, providing a model that can be dissected, but if an automated method was employed to... 

Fig. 10. Case study mD-QSAR methods 4D-QSAR. Two models (Models 2 and 4) are compared for Alignment 1. The only difference between the models is the two erroneous descriptors near the ethylamine of the compound. These two descriptors (3, -1, -1, any) and (3, -3, -2, any), for Models 2 and 4, respectively, provide misleading results.

Fig. 11. (see facing page) Manifold 4D-QSAR models. Three separate models are illustrated for the same set of compounds for Alignment 2. The models are ordered based on Q2 values with (A) being the best (0.767) and (C) the worst (0.619). 

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