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GQSAR allows flexibility to study various molecular fragments of interest in relation to the variation in biological response. Group or Fragment based QSAR is also known as GQSAR. This method gives mixed results and is generally not trusted to have accuracy of more than РІВС’.1 units. Fragmentary values have been determined statistically, based on empirical data for known logP values. It has been shown that the logP of compound can be determined by the sum of its fragments fragment-based methods are generally accepted as better predictors than atomic-based methods. Types Fragment based (group contribution) Р”Р„nalogously, the ' partition coefficient'-a measurement of differential solubility and itself a component of QSAR predictions-can be predicted either by atomic methods (known as 'XLogP' or 'ALogP') or by chemical fragment methods (known as 'CLogP' and other variations). The SAR paradox refers to the fact that it is not the case that all similar molecules have similar activities. Created hypotheses usually rely on a finite number of chemicals, so care must be taken to avoid overfitting: the generation of hypotheses that fit training data very closely but perform poorly when applied to new data. In general, one is more interested in finding strong trends. Examples were given in the bioisosterism reviews by Patanie/LaVoie and Brown. reaction ability, biotransformation ability, solubility, target activity, and so on, might depend on another difference.

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The underlying problem is therefore how to define a small difference on a molecular level, since each kind of activity, e.g. This principle is also called StructureРІР‚вЂњActivity Relationship ( SAR). The basic assumption for all molecule-based hypotheses is that similar molecules have similar activities. Principal steps of QSAR/QSPR including (i) Selection of Data set and extraction of structural/empirical descriptors (ii) variable selection, (iii) model construction and (iv) validation evaluation.' SAR and the SAR paradox

5 Evaluation of the quality of QSAR models.

3.1 Fragment based (group contribution).

The error includes model error ( bias) and observational variability, that is, the variability in observations even on a correct model.

Activity = f (physiochemical properties and/or structural properties) + error.

Р”Р„ QSAR has the form of a mathematical model: The mathematical expression, if carefully validated can then be used to predict the modeled response of other chemical structures. Additionally, when physicochemical properties or structures are expressed by numbers, one can find a mathematical relationship, or quantitative structure-activity relationship, between the two. Some examples are quantitative structureРІР‚вЂњreactivity relationships (QSRRs), quantitative structureРІР‚вЂњchromatography relationships (QSCRs) and, quantitative structureРІР‚вЂњtoxicity relationships (QSTRs), quantitative structureРІР‚вЂњelectrochemistry relationships (QSERs), and quantitative structureРІР‚вЂњ biodegradability relationships (QSBRs).' Р”Р„s an example, biological activity can be expressed quantitatively as the concentration of a substance required to give a certain biological response. 'Different properties or behaviors of chemical molecules have been investigated in the field of QSPR. Related terms include quantitative structureРІР‚вЂњproperty relationships ( QSPR) when a chemical property is modeled as the response variable. Second, QSAR models predict the activities of new chemicals. QSAR models first summarize a supposed relationship between chemical structures and biological activity in a data-set of chemicals. In QSAR modeling, the predictors consist of physico-chemical properties or theoretical molecular descriptors of chemicals the QSAR response-variable could be a biological activity of the chemicals. Like other regression models, QSAR regression models relate a set of 'predictor' variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable. Quantitative structureРІР‚вЂњactivity relationship models ( QSAR models) are regression or classification models used in the chemical and biological sciences and engineering.