2D quantitative structure-activity relationship

The simplest methodology is two-dimensional (2D) quantitative structure-activity relationships (QSAR), in which calculated descriptors of molecules are related to an end point of interest via a mathematical relationship to estimate a numerical or categorical value for that end point. The mathematical relationship is fitted to a training set of compounds for which data for the end point has been measured experimentally. New molecules can then be described with the descriptors used in the model and their end point values predicted. 2D QSAR methods can be used to predict the interaction of compoimds with protein targets or antitargets and are widely used for prediction of physicochemical and ADME properties, such as hpophilicity, solubility, hiunan intestinal absorption, and blood-brain barrier penetration [18]. An excellent review of the strategies and pitfalls of 2D QSAR has been published by Lewis and Wood [19]. 

D-QSAR Two-dimensional quantitative structure-activity relationships 3D-QSAR Three-dimensional quantitative structure-activity relationships... 

In addition, traditional quantitative structure-activity relationship (QSAR) models were reported. Gozalbes et al. attempted to predict the blood-brain barrier permeabilities of four arylacetamides using linear discriminant analysis [65], while Medina-Franco et al. discriminated between active and inactive BCG compounds using two-dimensional (2D) and three-dimensional (3D) structural-similarity methods [66]. 

Because of fhe stereospecifity of biological effects, QSAR (quantitative structure-activity relationships) methods must be capable of taking into account atomic chiralities. Indeed, one of fhe most popular 3D-QSAR methods, CoMFA and other CoMFA-like methods take into account chirality by default, since fhe molecular fields of chiral isomers are different If compounds are highly flexible and no experimental structural information about fhe receptor-ligand complexes is available, CoMFA (and CoMFA-like) methods are not always applicable. Several shortcomings and problems have motivated researchers to consider improvements to these techniques. The first idea for improvement was to modify the conventional 2D descriptors to make them chirahty-sensitive [1]. 

The Corina program can generate 3D coordinates for 2D structures rapidly [61] With the help of a 3D structure, it is possible to calculate energy of the molecule, volume, interatomic charge distribution, and other 3D descriptors required for quantitative structure-activity relationship (QSAR)-based predictive studies (Fig. 1.24). 

Rational drug design is based on the belief that the biological properties of drugs are related to their actual structural features. What has changed along the years is the way molecules are perceived and defined. In the past, medicinal chemists considered molecules as simple two-dimensional (2D) entities with related chemical and physicochemical properties. Quantitative structure-activity relationships (QSAR) concepts began to be considered and became very accepted. 

Artificial Intelligence in Chemistry Chemical Engineering Expert Systems Chemometrics Multivariate View on Chemical Problems Electrostatic Potentials Chemical Applications Environmental Chemistry QSAR Experimental Data Evaluation and Quality Control Fuzzy Methods in Chemistry Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry NMR Data Correlation with Chemical Structure Protein Modeling Protein Structure Prediction in ID, 2D, and 3D Quality Control, Data Analysis Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR) Shape Analysis Spectroscopic Databases Structure Determination by Computer-based Spectrum Interpretation. 

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, that is, their inability to distinguish stereoisomers. Examples of 3D QSAR include molecular shape analysis (MSA) [26], distance geometry,and Voronoi techniques [27]. The MSA method utilizes shape descriptors and MLR analysis, whereas the other two approaches apply atomic refractivity as structural descriptor and the solution of mathematical inequalities to obtain the quantitative relationships. These methods have been applied to study structure-activity relationships of many data sets by Hopfinger and Crippen, respectively. Perhaps the most popular example of the 3D QSAR is the com-... 

The starting dataset used to develop the 3D quantitative structure property relationship (3D-QSPR) model consisted of 370 commercially available compounds. Activity data and 2D structures were retrieved from the Cerep database [18]. Inhibition of CYP 3A4 was reported as inhibition of the formation of 6y9-hydroxy-tes-tosterone [19]. Ketoconazole was used as reference compound so that all values are expressed as percentages. The log of the normalized CYP3A4 inhibition per-... 

On the basis of the adjacency or distance matrices, many numerical constitutional descriptors associated with each molecnle [called topological indices (TIs)] were described starting with Wiener s (W) and Hosoya s indices (Z) ° which are integer numbers used in quantitative structure-activity (or structure-property) relationships (QSARs or QSPRs, respectively). Since these TIs are derived from constitutional graphs which represent atoms by vertices, and covalent bonds by edges, on formulas that are embedded in a two-dimensional space, they can be called 2D TIs. No TI... 

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. 

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