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| '''Quantitative structure–activity relationship''' models ('''QSAR''' models) are [[regression analysis|regression]] or classification models used in the chemical and biological sciences and engineering. 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. 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. QSAR models first summarize a supposed relationship between [[chemical structure]]s and [[biological activity]] in a data-set of chemicals. Second QSAR models [[predictive inference|predict]] the activities of new chemicals. Related terms include ''quantitative structure–property relationships'' (''QSPR'') when a chemical property is modeled as the response variable. In a nutshell, QSAR and QSPR tries to discern the relationship between molecular descriptors that describe the unique physico-chemical properties of the set of compounds of interest with their respective biological activity or chemical property.<ref name = "Nantasenamat_2009">{{cite journal | author = Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V | title = A practical overview of quantitative structure-activity relationship | journal = Excli J. | volume = 8 | pages = 74–88 | year = 2009 }}</ref><ref name = "Nantasenamat_2010">{{cite journal | author = Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V | title = Advances in computational methods to predict the biological activity of compounds | journal = Expert Opin. Drug Discov. | volume = 5 | pages = 633–54 | year = 2010 | doi = 10.1517/17460441.2010.492827 }}</ref>
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| For example, biological activity can be expressed quantitatively as the concentration of a substance required to give a certain biological response. Additionally, when physicochemical properties or structures are expressed by numbers, one can find a mathematical relationship, or quantitative structure-activity relationship, between the two. The mathematical expression, if carefully validated <ref name = "Tropsha_2003">{{cite journal | author = Tropsha A, Gramatica P, Gombar VJ | title = The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models | journal = QSAR &Comb. Sci. | volume = 22 | pages = 69–77 | year = 2003 | doi = 10.1002/qsar.200390007 }}</ref><ref name = "Gramatica_2007">{{cite journal | author = Gramatica P | title = Principles of QSAR models validation: internal and external | journal = QSAR &Comb. Sci. | volume = 26 | pages = 694–701| year = 2007 | doi = 10.1002/qsar.200610151 }}</ref><ref name="Chirico_Gramatica_2012">{{cite journal | author = Chirico N, Gramatica P | title = Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection | journal = J Chem Inf Model | volume = 52 | issue = 8 | pages = 2044–58 |date=August 2012 | pmid = 22721530 | doi = 10.1021/ci300084j }}</ref> can then be used to predict the modeled response of other chemical structures, by carefully verifying the Applicability domain (AD).
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| A QSAR has the form of a [[mathematical model]]:
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| * <math>\;\text{Activity} = f(\text{physicochemical properties and/or structural properties})+\text{Error}</math>
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| The error includes [[model error]] ([[bias of an estimator|bias]]) and observational variability, that is, the variability in observations even on a correct model.
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| == SAR and the SAR paradox ==
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| The basic assumption for all molecule based [[hypotheses]] is that similar molecules have similar activities. This principle is also called Structure–Activity Relationship ([[Structure–activity relationship|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. Good examples were given in the [[bioisosterism]] reviews by Patanie/LaVoie<ref name="pmid11848856">{{cite journal | author = Patani GA, LaVoie EJ | title = Bioisosterism: A Rational Approach in Drug Design | journal = Chemical Reviews | volume = 96 | issue = 8 | pages = 3147–3176 |date=December 1996 | pmid = 11848856 | doi = 10.1021/cr950066q | url = }}</ref> and Brown.<ref>Nathan Brown. ''Bioisosteres in Medicinal Chemistry''. Wiley-VCH, '''2012''', p. 237. ISBN 978-3-527-33015-7</ref>
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| In general, one is more interested in finding strong [[Trend estimation|trends]]. Created [[Hypothesis|hypotheses]] usually rely on a [[finite set|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.
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| The SAR paradox refers to the fact that it is not the case that all similar molecules have similar activities.
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| == Types ==
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| === Fragment based (group contribution) ===
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| Analogously, 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). It has been shown that the [[partition coefficient|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.<ref name="pmid17597897">{{cite journal | author = Thompson SJ, Hattotuwagama CK, Holliday JD, Flower DR | title = On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values | journal = Bioinformation | volume = 1 | issue = 7 | pages = 237–41 | year = 2006 | pmid = 17597897 | pmc = 1891704 | doi = }}</ref> Fragmentary values have been determined statistically, based on empirical data for known logP values. This method gives mixed results and is generally not trusted to have accuracy of more than ±0.1 units.<ref>{{Cite journal | title = Prediction of physicochemical parameters by atomic contributions | author = Wildman SA, Crippen GM | doi = 10.1021/ci990307l | year = 1999 | journal = J. Chem. Inf. Comput. Sci | pages = 868–873 | volume = 39 | issue = 5 }}</ref>
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| Group or Fragment based QSAR is also known as GQSAR.<ref name="Ajmani_2008"/> GQSAR allows flexibility to study various molecular fragments of interest in relation to the variation in biological response. The molecular fragments could be substituents at various substitution sites in congeneric set of molecules or could be on the basis of pre-defined chemical rules in case of non-congeneric set. GQSAR also considers cross-terms fragment descriptors, which could be helpful in identification of key fragment interactions in determining variation of activity.<ref name="Ajmani_2008">{{cite journal | author = Ajmani S, Jadhav K, Kulkarni SA | title = Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR|journal=QSAR & Combinatorial Science |date=November 2008 | volume = 28 | issue = 1 | pages = 36–51 | doi = 10.1002/qsar.200810063}}</ref>
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| Lead discovery using Fragnomics is an emerging paradigm. In this context FB-QSAR proves to be a promising strategy for fragment library design and in fragment-to-lead identification endeavours.<ref>{{cite journal | author = Manoharan P, Vijayan RSK , Ghoshal N | title = Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies
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| |journal= Journal of Computer-Aided Molecular Design |date=September 2010 | volume = 24 | issue = 10 | pmid = 20740315 | pages = 843–864 | doi = 10.1007/s10822-010-9378-9|bibcode = 2010JCAMD..24..843M }}</ref>
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| === 3D-QSAR ===
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| '''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 [[superimposition]]. 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), the hydrophobic regions (water-soluble surfaces),<ref>{{cite news|title=The Identification of Bioisosteres as Drug Development Candidates|author=Tim Cheeseright|publisher=Cresset BioMolecular Discovery|work=[[Cresset Biomolecular Discovery]]|url=http://www.cresset-group.com//publications/Cheeseright_IPT_09.pdf}}</ref> and the electrostatic fields.<ref name="isbn0-582-38210-6">{{cite book | author = Leach AR | title = Molecular modelling: principles and applications | edition = | language = | publisher = Prentice Hall | location = Englewood Cliffs, N.J | year = 2001 | pages = | isbn = 0-582-38210-6 }}</ref>
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| 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 name="isbn0-262-19509-7">{{cite book | author = Vert, J-P, Schölkopf B, Tsuda K | title = Kernel methods in computational biology | edition = | language = | publisher = MIT Press | location = Cambridge, Mass | year = 2004 | isbn = 0-262-19509-7 }}</ref> An alternative approach uses [[multiple-instance learning]] by encoding molecules as sets of data instances, each of which represents a possible molecular conformation. A label or response is assigned to each set corresponding to the activity of the molecule, which is assumed to be determined by at least one instance in the set (i.e. some conformation of the molecule).<ref>{{cite journal | author = Dietterich TG, Lathrop RH, Lozano-Pérez T | title = Solving the multiple instance problem with axis-parallel rectangles | journal = Artificial Intelligence | volume = 89 | issue = 1–2 | year = 1997 | pages = 31–71 | doi = 10.1016/S0004-3702(96)00034-3}}</ref>
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| [[File:3-D QSAutogrid-R MPGRS example image.png|thumb|3-D QSAutogrid-R MPGRS example image]] | |
| On June 18, 2011 the CoMFA patent has dropped any restriction on the use of GRID and PLS technologies and the RCMD team (www.rcmd.it) has opened a 3D QSAR web server (www.3d-qsar.com) based on the 3-D QSAutogrid/R engine.<ref name="pmid22643034">{{cite journal | author = Ballante F, Ragno R | title = 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications | journal = J Chem Inf Model | volume = 52 | issue = 6 | pages = 1674–85 |date=June 2012 | pmid = 22643034 | doi = 10.1021/ci300123x }}</ref> 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol and others.
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| === Chemical descriptor based ===
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| In this approach, descriptors quantifying various electronic, geometric, or steric properties of a catalyst are computed and used to develop a QSAR.<ref>{{cite journal | author = Caruthers JM, Lauterbach JA, Thomson KT, Venkatasubramanian V, Snively CM. Bhan A, Katare S, Oskarsdottir G | title = Catalyst design: knowledge extraction from high-throughput experimentation | journal = J. Catal. | year = 2003 | volume = 216 | pages = 3776–3777 | doi = 10.1016/S0021-9517(02)00036-2}}</ref> This approach is different from the fragment (or group contribution) approach in that the descriptors are computed for the system as whole rather than from the properties of individual fragments. This approach is different from the 3D-QSAR approach in that the descriptors are computed from scalar quantities (e.g., energies, geometric parameters) rather than from 3D fields. An example of this approach is the QSARs developed for olefin polymerization by [[half sandwich compound]]s.<ref name="pmid17348648">{{cite journal | author = Manz TA, Phomphrai K, Medvedev G, Krishnamurthy BB, Sharma S, Haq J, Novstrup KA, Thomson KT, Delgass WN, Caruthers JM, Abu-Omar MM | title = Structure-activity correlation in titanium single-site olefin polymerization catalysts containing mixed cyclopentadienyl/aryloxide ligation | journal = J. Am. Chem. Soc. | volume = 129 | issue = 13 | pages = 3776–7 |date=April 2007 | pmid = 17348648 | doi = 10.1021/ja0640849 }}</ref><ref name = "Organometallics2012">{{cite journal | author = Manz TA, Caruthers JM, Sharma S, Phomphrai K, Thomson KT, Delgass WN, Abu-Omar MM | title = Structure–Activity Correlation for Relative Chain Initiation to Propagation Rates in Single-Site Olefin Polymerization Catalysis | journal = Organometallics | year = 2012 | volume = 31 | pages = 602–618 | doi = 10.1021/om200884x | issue = 2}}</ref>
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| == Modeling ==
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| In the literature it can be often found that chemists have a preference for [[partial least squares]] (PLS) methods,{{citation needed|date=April 2012}} since it applies the [[feature extraction]] and [[inductive reasoning|induction]] in one step.
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| === Data mining approach ===
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| Computer SAR models typically calculate a relatively large number of features. Because those lack structural interpretation ability, the preprocessing steps face a [[feature selection]] problem (i.e., which structural features should be interpreted to determine the structure-activity relationship). Feature selection can be accomplished by visual inspection (qualitative selection by a human); by data mining; or by molecule mining.
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| 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.
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| [[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 name="isbn0-521-58519-8">{{cite book | author = Gusfield D | authorlink = | editor = | others = | title = Algorithms on strings, trees, and sequences: computer science and computational biology | edition = | language = | publisher = Cambridge University Press | location = Cambridge, UK | year = 1997 | origyear = | pages = | quote = | isbn = 0-521-58519-8 | oclc = | doi = | url = | accessdate = }}</ref><ref name="isbn0-8247-2397-X">{{cite book | author = Helma C | title = Predictive toxicology | edition = | language = | publisher = Taylor & Francis | location = Washington, DC | year = 2005 | isbn = 0-8247-2397-X | oclc = | doi = | url = | accessdate = }}</ref>
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| == Evaluation of the quality of QSAR models ==
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| QSAR modeling produces predictive [[statistical model|model]]s derived from application of statistical tools correlating [[biological activity]] (including desirable therapeutic effect and undesirable side effects)or physico-chemical properties in QSPR models of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of [[molecular geometry|molecular structure]] and/or [[molecular property|properties]]. QSARs are being applied in many disciplines for example [[risk assessment]], toxicity prediction, and regulatory decisions<ref name="Tong_2005">{{cite journal | author = Tong W, Hong H, Xie Q, Shi L, Fang H, Perkins R | title = Assessing QSAR Limitations – A Regulatory Perspective | journal = Current Computer-Aided Drug Design | volume = 1 | issue = 2 | pages = 195–205 |date=April 2005 | pmid = | doi = 10.2174/1573409053585663 }}</ref> in addition to [[drug discovery]] and [[drug development|lead optimization]].<ref name="pmid13677480">{{cite journal | author = Dearden JC | title = In silico prediction of drug toxicity | journal = Journal of Computer-aided Molecular Design | volume = 17 | issue = 2–4 | pages = 119–27 | year = 2003 | pmid = 13677480 | doi = 10.1023/A:1025361621494 |bibcode = 2003JCAMD..17..119D }}</ref> Obtaining a good quality QSAR model depends on many factors, such as the quality of input data, the choice of descriptors and statistical methods for modeling and for validation. Any QSAR modeling should ultimately lead to statistically robust and predictive models capable of making accurate and reliable predictions of the modeled response of new compounds.
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| For validation of QSAR models usually various strategies are adopted:<ref name="isbn3-527-30044-9">{{cite book | author = Wold S, Eriksson L | authorlink = | editor = Waterbeemd, Han van de | others = | title = Chemometric methods in molecular design | edition = | language = | publisher = VCH | location = Weinheim | year = 1995 | origyear = | pages = 309–318 | chapter = Statistical validation of QSAR results | quote = | isbn = 3-527-30044-9 | oclc = | doi = | url = | accessdate = }}</ref>
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| # internal validation or [[iterative cross-validation (statistics)|cross-validation]];
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| # external validation by splitting the available data set into training set for model development and prediction set for model predictivity check;
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| # blind external validation by application of model on new external data and
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| # data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors.
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| The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain of the models.<ref name = "Tropsha_2003"/><ref name = "Gramatica_2007"/><ref name="Chirico_Gramatica_2012"/><ref name=Roy2007>{{cite journal | author = Roy, K | title = On some aspects of validation of predictive quantitative structure-activity relationship models | journal = Expert Opin. Drug Discov. | volume = 2 | issue = 12 | pages = 1567–1577 | year = 2007 | pmid = | doi = 10.1517/17460441.2.12.1567 }}</ref> Leave one-out cross-validation generally leads to an overestimation of predictive capacity, and even with external validation, no one can be sure whether the selection of training and test sets was manipulated to maximize the predictive capacity of the model being published. Different aspects of validation of QSAR models that need attention includes methods of selection of training set compounds,<ref>{{cite journal | author = Leonard JT, Roy K | title = On selection of training and test sets for the development of predictive QSAR models | journal = QSAR & Combinatorial Science | volume = 25 | issue = 3 | pages = | year = 2006 | pmid = | doi = 10.1002/qsar.200510161 }}</ref> setting training set size<ref>{{cite journal | author = Roy PP, Leonard JT, Roy K | title = Exploring the impact of size of training sets for the development of predictive QSAR models | journal = Chemometrics and Intelligent Laboratory Systems | volume = 90 | issue = 1 | pages = 31–42 | year = 2008 | pmid = | doi = 10.1016/j.chemolab.2007.07.004 }}</ref> and impact of variable selection<ref name="pmid17933600">{{cite journal | author = Put R, Vander Heyden Y | title = Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships | journal = Anal. Chim. Acta | volume = 602 | issue = 2 | pages = 164–72 |date=October 2007 | pmid = 17933600 | doi = 10.1016/j.aca.2007.09.014 }}</ref> for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important.<ref name="Chirico_Gramatica_2012"/><ref name="Roy_2009">{{cite journal | author = Pratim Roy P, Paul S, Mitra I, Roy K | title = On two novel parameters for validation of predictive QSAR models | journal = Molecules | volume = 14 | issue = 5 | pages = 1660–701 | year = 2009 | pmid = 19471190 | doi = 10.3390/molecules14051660 | url = }}</ref><ref name="pmid21800825">{{cite journal | author = Chirico N, Gramatica P | title = Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient | journal = J Chem Inf Model | volume = 51 | issue = 9 | pages = 2320–35 |date=September 2011 | pmid = 21800825 | doi = 10.1021/ci200211n }}</ref>
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| == Application ==
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| === Chemical ===
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| One of the first [[history|historical]] QSAR applications was to predict [[boiling point]]s.<ref name="isbn0-85626-454-7">{{cite book | author = Rouvray DH, Bonchev D | others = | title = Chemical graph theory: introduction and fundamentals | edition = | language = | publisher = Abacus Press | location = Tunbridge Wells, Kent, England | year = 1991 | isbn = 0-85626-454-7 }}</ref>
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| It is well known for instance that within a particular [[chemical classification|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]].
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| A still very interesting application is the [[Hammett equation]], [[Taft equation]] and [[Acid dissociation constant|pKa prediction]] methods.<ref name="CMC2_2007">{{cite book | author = Fraczkiewicz R | title = Comprehensive medicinal chemistry II | edition = | language = | publisher = Elsevier | location = Amsterdam | year = 2007 | origyear = | pages = | chapter = In Silico Prediction of Ionization | quote = | isbn = 0-08-044518-7 }}</ref>
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| === Biological ===
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| 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 "[[druglikeness]]" according to [[Lipinski's Rule of Five]].
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| 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 name="pmid12668435">{{cite journal | author = Freyhult EK, Andersson K, Gustafsson MG | title = Structural Modeling Extends QSAR Analysis of Antibody-Lysozyme Interactions to 3D-QSAR | journal = Biophysical Journal | volume = 84 | issue = 4 | pages = 2264–72 |date=April 2003 | pmid = 12668435 | pmc = 1302793 | doi = 10.1016/S0006-3495(03)75032-2 | url = | bibcode=2003BpJ....84.2264F}}</ref>
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| 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 (social sciences)|coding]]<ref name="isbn3-527-29913-0">{{cite book | author = Timmerman H, Todeschini R, Consonni V, Mannhold R, Kubinyi H | authorlink = | editor = | others = | title = Handbook of Molecular Descriptors | publisher = Wiley-VCH | location = Weinheim | year = 2002 | isbn = 3-527-29913-0 }}</ref> and [[learning]].<ref name="isbn0-471-05669-3">{{cite book | author = Duda RO, Hart PW, Stork DG | title = Pattern classification | edition = | language = | publisher = John Wiley & Sons | location = Chichester | year = 2001 | isbn = 0-471-05669-3 }}</ref>
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| === Applications ===
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| (Q)SAR models have been used for the [[risk management]] of chemicals risk. QSARS are suggested by regulatory authorities; in the [[European Union]], QSARs are suggested by the [[Registration, Evaluation, Authorisation and Restriction of Chemicals|REACH]] regulation, where "REACH" abbreviates "Registration, Evaluation, Authorisation and Restriction of Chemicals".
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| The chemical descriptor space whose [[convex hull]] is generated by a particular training set of chemicals is called the training set's [[applicability domain]]. Prediction of properties of novel chemicals that are located outside the applicability domain uses [[extrapolation]], and so is less reliable (on average) than prediction within the applicability domain. The assessment of the reliability of QSAR predictions remains a research topic.
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| == See also ==
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| {{columns-list|2|
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| *[[ADME]]
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| *[[Cheminformatics]]
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| *[[Computer-assisted drug design]] (CADD)
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| *[[Conformation Activity Relationship]]
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| *[[Differential solubility]]
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| *[[Molecular design software]]
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| *[[Partition coefficient]]
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| *[[Pharmacokinetics]]
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| *[[Pharmacophore]]
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| *[[QSAR & Combinatorial Science]] – [[Scientific journal]]
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| *[[List of software for molecular mechanics modeling|Software for molecular mechanics modeling]]
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| * [[Chemicalize.org]]:[[Chemicalize.org#List_of_the_predicted_structure_based_properties|List of predicted structure based properties]]
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| }}
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| == References ==
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| {{Reflist|35em}}
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| == Further reading ==
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| {{refbegin}}
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| * {{cite book | author = Selassie CD | authorlink = | editor = Abraham DJ | others = | title = Burger's medicinal Chemistry and Drug Discovery | edition = 6th | volume = 1 | publisher = Wiley | location = New York | year = 2003 | origyear = | pages = 1–48 | quote = | isbn = 0-471-27401-1 | oclc = | doi = | url = | chapter = History of Quantitative Structure-Activity Relationships | chapterurl = http://media.wiley.com/product_data/excerpt/03/04712709/0471270903.pdf }}
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| {{refend}}
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| == External links ==
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| * {{cite web | url = http://www.qsar.org/ | title = The Cheminformatics and QSAR Society | author = | authorlink = | coauthors = | date = | format = | work = | publisher = | pages = | language = | archiveurl = | archivedate = | quote = | accessdate = 2009-05-11}}
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| * {{cite web | url = http://www.3d-qsar.com/ | title = The 3D QSAR Server | author = | authorlink = | coauthors = | date = | format = | work = | publisher = | pages = | language = | archiveurl = | archivedate = | quote = | accessdate = 2011-06-18}}
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| * {{cite web | url = http://www.natureprotocols.com/2007/03/05/development_of_qsar_models_usi_1.php | title = Nature Protocols: Development of QSAR models using C-QSAR program | author = | authorlink = | coauthors = | date = | format = | work = | publisher = Nature Protocols | pages = | language = | archiveurl = | archivedate = | quote = A regression program that has dual databases of over 21,000 QSAR models | doi = 10.1038/nprot.2007.125 | accessdate = 2009-05-11}}
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| * {{cite web | url = http://www.qsarworld.com | title = QSAR World | author = | authorlink = | coauthors = | date = | format = | work = | publisher = | pages = | language = | archiveurl = | archivedate = | quote = A comprehensive web resource for QSAR modelers | accessdate = 2009-05-11}}
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| * [http://dtclab.webs.com/software-tools Chemoinformatics Tools], Drug Theoretics and Cheminformatics Laboratory
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| {{Medicinal chemistry}}
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| {{DEFAULTSORT:Quantitative structure-activity relationship}}
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| [[Category:Medicinal chemistry]]
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| [[Category:Pharmacology]]
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| [[Category:Cheminformatics]]
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| [[Category:Computational chemistry]]
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| [[Category:Paradoxes|Structure-Activity Relationship paradox]]
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