Structure-activity relationship generating

Horvath, D., Bonachera, E., Solov ev, V, Gaudin, C. and Varnek, A. (2007) Stochastic versus stepwise strategies for quantitative structure-activity relationship generations how much effort may the mining for successful QSAR models take J. Chem. Inf. Model, 47, 927-939. 

Neural networks have been proposed as an alternative way to generate quantitative structure-activity relationships [Andrea and Kalayeh 1991]. A commonly used type of neural net contains layers of units with connections between all pairs of units in adjacent layers (Figure 12.38). Each unit is in a state represented by a real value between 0 and 1. The state of a unit is determined by the states of the units in the previous layer to which it is connected and the strengths of the weights on these connections. A neural net must first be trained to perform the desired task. To do this, the network is presented with a... 

Bis(oxazohnes) figands have been so widely used for the Diels-Alder reaction between N-2-alkenoyl-l,3-oxazolidine-2-one and cyclopentadiene that Lipkowitz and Pradhan developed a QSAR (quantitative structure-activity relationship) using Comparative Molecular Field Analysis (CoMFA) for a set of 23 copper-catalysts containing mainly bis(oxazoline) figands. The generated... 

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-... 

Because of the large number of chemicals of actual and potential concern, the difficulties and cost of experimental determinations, and scientific interest in elucidating the fundamental molecular determinants of physical-chemical properties, considerable effort has been devoted to generating quantitative structure-property relationships (QSPRs). This concept of structure-property relationships or structure-activity relationships (QSARs) is based on observations of linear free-energy relationships, and usually takes the form of a plot or regression of the property of interest as a function of an appropriate molecular descriptor which can be calculated using only a knowledge of molecular structure or a readily accessible molecular property. 

The early empirical structure-activity relationships promoted discovery of second-generation anticancer drugs such as complexes 2 and 3. However, analogs of these drugs usually display similar clinical profiles to the parent drugs. Therefore new classes of platinum complexes are required with distinct properties. 

Baroni, M., Costantino, G., Cruciani, G., Riganelli, D., Valigi, R. and Clementi, S. (1993) Generating optimal linear PLS estimations (GOLPE) an advanced chemometric tool for handling 3D-QSAR problems. Quantitative Structure-Activity Relationships, 12, 9-20. 

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