View source for Quantitative structure-activity relationship

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==Overview==
'''Quantitative structure-activity relationship (QSAR)''' is the process by which [[chemical structure]] is quantitatively [[correlation|correlated]] with a well defined process, such as [[biological activity]] or chemical reactivity.

For example, biological activity can be expressed quantitatively as in the concentration of a substance required to give a certain biological response.  Additionally, when physiochemical properties or structures are expressed by numbers, one can form a mathematical relationship, or quantitative structure-activity relationship, between the two.  The mathematical expression can then be used to predict the biological response of other chemical structures.

QSAR's most general mathematical form is:
:Activity = ''f''(physiochemical properties and/or structural properties)

==SAR and SAR paradox==
The basic assumption for all molecule based [[hypotheses]] is that similar molecules have similar activities. This principle is also called '''S'''tructure-'''A'''ctivity '''R'''elationship (SAR). The underlying problem is therefore how to define a ''small'' difference on a molecular level, since each kind of activity, e.g. [[Chemical reaction|reaction]] ability, [[biotransformation]] ability, [[solubility]], target activity, and so on, might depend on another difference. A good example was given in the [[bioisosterism]] review of Patanie/LaVoie.<ref>G. A. Patani, E. J. LaVoie, ''Bioisosterism: A Rational Approach in Drug Design''. Chem. Rev., 1996, 96, 3147-3176. {{doi|10.1021/cr950066q}}</ref>

In general, one is more interested in finding strong trends. Created [[Hypothesis|hypotheses]] usually rely on a finite number of chemical data. Thus, the [[Induction (philosophy)|induction principle]] should be respected to avoid [[Overfitting|overfitted]] hypotheses and deriving overfitted and useless interpretations on structural/molecular data.

The SAR paradox refers to the fact that it is not the case that all similar molecules have similar activities.

==Applications==
===Chemical===
One of the first historical QSAR applications was to predict boiling points.<ref>D. Bonchev, D.H. Rouvray: ''Chemical Graph Theory: Introduction and Fundamentals''. Gordon and Breach Science Publishers, 1990, ISBN 0-85626-454-7.</ref>

It is well known for instance that within a particular family of [[chemical compound]]s, especially of [[organic chemistry]], that there are strong [[correlation]]s between structure and observed properties. A simple example is the relationship between the number of carbons in [[alkanes]] and their [[boiling point]]s. There is a clear trend in the increase of boiling point with an increase in the number carbons and this serves as a means for predicting the boiling points of [[higher alkanes]].

A still very interesting application is the [[Hammett equation]], [[Taft equation]] and [[Acid dissociation constant|pKa prediction]] methods.

===Biological===
The biological activity of molecules is usually measured in [[assay]]s to establish the level of inhibition of particular [[signal transduction]] or [[metabolic pathway]]s. Chemicals can also be biologically active by being [[toxicity|toxic]].  [[Drug discovery]] often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific [[biological target|targets]] and have low [[toxicity]] (non-specific activity).  Of special interest is the prediction of [[partition coefficient]] log ''P'', which is an important measure used in identifying "[[drug-likeness]]" according to [[Lipinski's Rule of Five]]. 

While many quantitative structure activity relationship analyses involve the interactions of a family of molecules with an [[enzyme]] or [[receptor (biochemistry)|receptor]] binding site, QSAR can also be used to study the interactions between the [[structural domain]]s of proteins. Protein-protein interactions can be quantitatively analyzed for structural variations resulted from [[site-directed mutagenesis]].<ref>E. K. Freyhult, K. Andersson, M. G. Gustafsson, ''Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR'',J. Biophys., '''2003''',  ''84'', {{ISSN|2264-2272}}. PMID 12668435</ref>.


It is part of the [[machine learning]] method to reduce the risk for a SAR paradox, especially taking into account that only a finite amount of data is available (see also [[Minimum-variance unbiased estimator|MVUE]]). In general all QSAR problems can be divided into a coding<ref>R. Todeschini, V. Consonni, ''Handbook of Molecular Descriptors'', Wiley-VCH, '''2000'''. ISBN 3527299130</ref> and [[learning]]<ref>R.O. Duda, P.E. Hart, D.G. Stork, ''Pattern Classification'', John Wiley & Sons, '''2001'''. ISBN 0-471-05669-3</ref> part.

===Data mining===
For the coding usually a relatively large number of features or molecular descriptors is calculated, which can lack structural interpretation ability. In combination with the later applied learning method or as preprocessing step occurs a feature selection problem. 

A typical [[data mining]] based prediction uses e.g. [[support vector machine]]s, [[decision tree]]s, [[neural networks]] for [[inductive reasoning|inducing]] a predictive learning model.

====3D-QSAR====
'''3D-QSAR''' refers to the application of [[Force field (chemistry)|force field]] calculations requiring three-dimensional structures, e.g. based on protein [[crystallography]] or molecule superposition. It uses computed potentials, e.g. the [[Lennard-Jones potential]], rather than experimental constants and is concerned with the overall molecule rather than a single substituent. It examines the steric fields (shape of the molecule) and the electrostatic fields based on the applied energy function.<ref>A. Leach, ''Molecular Modelling: Principles and Applications'', Prentice Hall, '''2001'''. ISBN 0-582-38210-6</ref>

The created data space is then usually reduced by a following [[feature extraction]] (see also [[dimensionality reduction]]). The following learning method can be any of the already mentioned [[machine learning]] methods, e.g. [[support vector machine]]s.<ref>Schölkopf, B., K. Tsuda and J. P. Vert: ''Kernel Methods in Computational Biology'', MIT Press, Cambridge, MA, '''2004'''.</ref>

In the literature it can be often found that chemists have a preference for [[partial least squares]] (PLS) methods, since  it applies the [[feature extraction]] and [[inductive reasoning|induction]] in one step.

===Molecule mining===
[[Molecule mining]] approaches, a special case of [[structured data mining]] approaches, apply a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. Furthermore there exist also approaches using [[Maximum common subgraph isomorphism problem|maximum common subgraph]] searches or [[graph kernel]]s.<ref>Gusfield, D., ''Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology'', Cambridge University Press, '''1997'''. ISBN 0-521-58519-8</ref>
<ref>C. Helma (ed.), ''Predictive Toxicology'', CRC, '''2005'''. ISBN 0-8247-2397-X</ref>

====Fragment based (group contribution)====
It has been shown that the [[logP]] of compound can be determined by the sum of its fragments. Fragmentary logP values have been determined statistically. This method gives mixed results and is generally not trusted to have accuracy of more than +/- 0.1 units.<ref>S. A. Wildman, G. M. Crippen, ''Prediction of Physicochemical Parameters by Atomic Contributions'', J. Chem. Inf. Comput. Sci.}, '''1999''', ''39'', 868-873. {{DOI|10.1021/ci990307l}}</ref>

== Applicability Domain ==
As the use of (Q)SAR models for chemical risk management increases steadily and is also used for regulatory purposes (in the EU: [[Registration, Evaluation, Authorisation and Restriction of Chemicals]]), it is of crucial importance be able to assess the reliability of predictions. The chemical descriptor space spanned by a particular training set of chemicals is called [[Applicability Domain]]. It offers the opportunity to assess whether a compound can be reliably predicted.

==See also==
*[[Structure-activity relationship ]]
*[[Cheminformatics]]
*[[ADME]]
*[[Differential solubility]]
*[[Intermolecular force]]
*[[Pharmacokinetics]]
*[[Pharmacophore]]
*[[CLogP]]
*[[Computer-assisted]] [[drug design]] (CADD)
*[[Protein structure prediction]]
*[[QSAR & Combinatorial Science]] - [[Scientific journal]]
*[[Software for molecular mechanics modeling]]

==References==
<references/>

==External links==
* [http://www.qsarworld.com QSAR World - A comprehensive web resource for QSAR modelers]
* [http://media.wiley.com/product_data/excerpt/03/04712709/0471270903.pdf History of QSAR] - (PDF)
* [http://www.natureprotocols.com/2007/03/05/development_of_qsar_models_usi_1.php Development of QSAR models using C-QSAR program: a regression program that has dual databases of over 21,000 QSAR models (a protocol)]
*[http://www.syrres.com/qsar2008  The 13th International Workshop on Quantitative Structure-Activity Relationships (QSARs) in the Environmental Sciences]
*[http://www.qsar.org/ The Cheminformatics and QSAR Society] 


{{Medicinal chemistry}}

[[Category:Medicinal chemistry]]
[[Category:Pharmacology]]
[[Category:Cheminformatics]]
[[Category:Paradoxes|Structure-Activity Relationship paradox]]

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