Structure-selectivity relationships subsets

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Quantitative Structure-Activity Relationship models are used increasingly in chemical data mining and combinatorial library design [5, 6]. For example, three-dimensional (3-D) stereoelectronic pharmacophore based on QSAR modeling was used recently to search the National Cancer Institute Repository of Small Molecules [7] to find new leads for inhibiting HIV type 1 reverse transcriptase at the nonnucleoside binding site [8]. A descriptor pharmacophore concept was introduced by us recently [9] on the basis of variable selection QSAR the descriptor pharmacophore is defined as a subset of... 

One may think of an iterative model for the preclinical discovery screening cycle. A large number of compounds are to be mined for compounds that are active for example, that bind to a particular target. The compounds may come from different sources such as vendor catalogues, corporate collections, or combinatorial chemistry projects. In fact, the compounds need only to exist in a virtual sense, because in silico predictions in the form of a model can be made in a virtual screen (Section 8) which can then be used to decide which compounds should be physically made and tested. A mapping from the structure space of compounds to the descriptor space or property space provides covariates or explanatory variables that can be used to build predictive models. These models can help in the selection process, where a subset of available molecules is chosen for the biological screen. The experimental results of the biological screen (actives and inactives, or numeric potency values) are then used to learn more about the structure-activity relationship (SAR) which leads to new models and a new selection of compounds as the cycle renews. 

Of course, the application of experimental design to pharmaceutical problems is not new. It has been used for the design of structure-activity relationship (SAR) compound sets, "- for optimizing synthetic processes, in analytical chemistry, and for selecting screening subsets from corporate chemical archives. This chapter focuses only on the experimental design of combi-... 

Sutter and co-workers reported a method for automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing (36,132). The cost function used to evaluate the effectiveness of the deseriptors was based on a neural network. The result is an automated descriptor selection algorithm that is an optimization inside of an optimization. Application of the method to QSAR shows that effective descriptor subsets are found, and they support models that are as good or better than those obtained using traditional linear regression methods. 

In principle, there are three main steps required to carry out diversity-based subset selections (1) the calculation of descriptors representing the compound structures, (2) a quantitative method to describe the similarity or dissimilarity of molecules in relationship to each other, and (3) selection methods to identify compounds based on their similarity or dissimilarity values that best represent the entire compound set. In the following, the three steps are described in more detail. 

The process of constructing a QSPR model includes the following steps, summarized in Fig.5.1 (1) selection of a data set (2) generation of various structural descriptors by means of MD simulations application of variable selection or/and data reduction methods on the calculated descriptors in order to identify a small subset of these descriptors that are relevant to the macroscopic material properties being modeled (in some cases this step may not be required) (3) generation of linear/multilinear or non-Unear relationship between the descriptors and the global material property (4) validation of the model to assess its reliability, robustness, predictivity, and domain of applicability. 

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