定量的構造活性相関

関: 3D-QSAR、quantitative structure-activity relationship

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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2018/07/19 15:10:31」(JST)

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定量的構造活性相関(ていりょうてきこうぞうかっせいそうかん)は化学物質の構造と生物学的（薬学的あるいは毒性学的）な活性との間になりたつ量的関係のこと。これにより構造的に類似した化合物の「薬効」について予測することを目的とする。QSAR(=Quantitative Structure-Activity(またはAffinity) Relationshipの略)と呼ばれることもある。QSARを英語では「クェイサー」、日本語では「キューサー」と発音することが多い。

それに対し化学構造と物理的性質との関係を定量的構造物性相関（QSPR、Quantitative Structure-Property Relationship）という。両者は密接な関係があり方法論的にも共通する部分が多い。

コーウィン・ハンシュによって研究が始められ、1964年にハンシュと藤田稔夫が発表した方法（ハンシュ-藤田法）が代表的な方法として知られる。

方法としては、化合物の疎水性、対象とする化合物の構造を表現する数量（幾何学的構造を表す記述子、HOMOやLUMO(フロンティア軌道理論参照)のエネルギー、あるいはハメットの置換基定数、電気陰性度といった電子的記述子など）を抽出し、構造的に類似する一連の物質に関してこれら数量と活性との関係を統計学的に（回帰分析などを用い）検討する。

なお、記述子としては化合物に関するパラメタを使用しているが、基本的には薬物標的分子と化合物との相互作用を前提とした手法であり、実際に定量的構造活性相関研究の結果から薬物標的-化合物間の相互作用様式を推定するといった使用法もしばしば見られる。

計算化学の一部門であり、方法的には計算機化学ということができる。

1 Essential steps in QSAR studies
2 SAR and the SAR paradox
3 Types
- 3.1 Fragment based (group contribution)
- 3.2 3D-QSAR
- 3.3 Chemical descriptor based
4 Modeling
- 4.1 Data mining approach
- 4.2 Matched molecular pair analysis
5 Evaluation of the quality of QSAR models
6 Application
- 6.1 Chemical
- 6.2 Biological
- 6.3 Applications
7 See also
8 References
9 Further reading
10 External links

Essential steps in QSAR studies

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."^[3]

SAR and the SAR paradox

The basic assumption for all molecule based hypotheses is that similar molecules have similar activities. This principle is also called 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. 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^[8] and Brown.^[9]

In general, one is more interested in finding strong trends. Created hypotheses usually rely on a finite number of chemical data. Thus, the induction principle should be respected to avoid 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.

Types

Fragment based (group contribution)

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 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.^[10] 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.^[11]

Group or Fragment based QSAR is also known as GQSAR.^[12] 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 sets. GQSAR also considers cross-terms fragment descriptors, which could be helpful in identification of key fragment interactions in determining variation of activity.^[12]

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.^[13]

An advanced approach on fragment or group-based QSAR based on the concept of pharmacophore-similarity is developed.^[14] This method, pharmacophore-similarity-based QSAR (PS-QSAR) uses topological pharmacophoric descriptors to develop QSAR models. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects.^[14]

3D-QSAR

The acronym 3D-QSAR or 3-D QSAR refers to the application of force field calculations requiring three-dimensional structures of a given set of small molecules with known activities (training set). The training set needs to be superimposed (aligned) by either experimental data (e.g. based on ligand-protein crystallography) or molecule superimposition software. 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. The first 3-D QSAR was named Comparative Molecular Field Analysis (CoMFA) by Cramer et al. It examined the steric fields (shape of the molecule) and the electrostatic fields^[15] which were correlated by means of partial least squares regression (PLS).

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 machines.^[16] 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).^[17]

On June 18, 2011 the Comparative Molecular Field Analysis (CoMFA) patent has dropped any restriction on the use of GRID and partial least-squares (PLS) technologies.^{[citation needed]}

Chemical descriptor based

In this approach, descriptors quantifying various electronic, geometric, or steric properties of a molecule are computed and used to develop a QSAR.^[18] 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 compounds.^[19]^[20]

Modeling

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

Data mining approach

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.

A typical data mining based prediction uses e.g. support vector machines, decision trees, artificial neural networks for inducing a predictive learning model.

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 searches or graph kernels.^[21]^[22]

QSAR protocol

Matched molecular pair analysis

Typically QSAR models derived from non linear machine learning is seen as a "black box", which fails to guide medicinal chemists. Recently there is a relatively new concept of matched molecular pair analysis^[23] or prediction driven MMPA which is coupled with QSAR model in order to identify activity cliffs.^[24]

Evaluation of the quality of QSAR models

QSAR modeling produces predictive models 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 structure or properties. QSARs are being applied in many disciplines, for example: risk assessment, toxicity prediction, and regulatory decisions^[25] in addition to drug discovery and lead optimization.^[26] 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.

For validation of QSAR models, usually various strategies are adopted:^[27]

internal validation or cross-validation (actually, while extracting data, cross validation is a measure of model robustness, the more a model is robust (higher q2) the less data extraction perturb the original model);
external validation by splitting the available data set into training set for model development and prediction set for model predictivity check;
blind external validation by application of model on new external data and
data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors.

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 (AD) of the models.^[4]^[5]^[6]^[28]

Some validation methodologies can be problematic. For example, leave one-out cross-validation generally leads to an overestimation of predictive capacity. Even with external validation, it is difficult to determine 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,^[29] setting training set size^[30] and impact of variable selection^[31] for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important.^[6]^[32]^[33]

Application

Chemical

One of the first historical QSAR applications was to predict boiling points.^[34]

It is well known for instance that within a particular family of chemical compounds, especially of organic chemistry, that there are strong correlations between structure and observed properties. A simple example is the relationship between the number of carbons in alkanes and their boiling points. 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 pKa prediction methods.^[35]

Biological

The biological activity of molecules is usually measured in assays to establish the level of inhibition of particular signal transduction or metabolic pathways. Drug discovery often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific 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.

While many quantitative structure activity relationship analyses involve the interactions of a family of molecules with an enzyme or receptor binding site, QSAR can also be used to study the interactions between the structural domains of proteins. Protein-protein interactions can be quantitatively analyzed for structural variations resulted from site-directed mutagenesis.^[36]

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 MVUE). In general, all QSAR problems can be divided into coding^[37]

and learning.^[38]

Applications

(Q)SAR models have been used for risk management. QSARS are suggested by regulatory authorities; in the European Union, QSARs are suggested by the REACH regulation, where "REACH" abbreviates "Registration, Evaluation, Authorisation and Restriction of Chemicals".

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.

The QSAR equations can be used to predict biological activities of newer molecules before their synthesis.

Examples of machine learning tools for QSAR modeling include:^[39]

S.No.	Name	Algorithms	External link
1.	R	RF,SVM, Naïve Bayesian, and ANN	"R: The R Project for Statistical Computing".
2.	libSVM	SVM	"LIBSVM -- A Library for Support Vector Machines".
3.	Orange	RF, SVM, and Naïve Bayesian	"Orange Data Mining".
4.	RapidMiner	SVM, RF, Naïve Bayes, DT, ANN, and k-NN	"RapidMiner \| #1 Open Source Predictive Analytics Platform".
5.	Weka	RF, SVM, and Naïve Bayes	"Weka 3 - Data Mining with Open Source Machine Learning Software in Java".
6.	Knime	DT, Naïve Bayes, and SVM	"KNIME \| Open for Innovation".
7.	AZOrange^[40]	RT, SVM, ANN, and RF	"AZCompTox/AZOrange: AstraZeneca add-ons to Orange". GitHub.
8.	Tanagra	SVM, RF, Naïve Bayes, and DT	"TANAGRA - A free DATA MINING software for teaching and research".
9.	Elki	k-NN	"ELKI Data Mining Framework".
10.	MALLET		"MALLET homepage".
11.	MOA		"MOA Massive Online Analysis \| Real Time Analytics for Data Streams".
12.	Deep Chem	Logistic Regression, Naive Bayes, RF, ANN, and others	"DeepChem". deepchem.io. Retrieved 20 October 2017.

References

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External links

"The Cheminformatics and QSAR Society". Retrieved 2009-05-11.
"The 3D QSAR Server". Retrieved 2011-06-18.
"Nature Protocols: Development of QSAR models using C-QSAR program". Nature Protocols. doi:10.1038/nprot.2007.125. Archived from the original on 2007-05-01. Retrieved 2009-05-11. A regression program that has dual databases of over 21,000 QSAR models
"QSAR World". Archived from the original on 2009-04-25. Retrieved 2009-05-11. A comprehensive web resource for QSAR modelers
Chemoinformatics Tools, Drug Theoretics and Cheminformatics Laboratory
Multiscale Conceptual Model Figures for QSARs in Biological and Environmental Science

English Journal

Study on the interaction between carbonyl-fused N-confused porphyrin and bovine serum albumin by spectroscopic techniques.

Yu X1, Liao Z2, Jiang B2, Zheng L2, Li X3.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.Spectrochim Acta A Mol Biomol Spectrosc.2014 Dec 10;133:372-7. doi: 10.1016/j.saa.2014.05.085. Epub 2014 Jun 11.
The interaction between carbonyl-fused N-confused porphyrin (CF-NCP) and bovine serum albumin (BSA) was investigated by fluorescence and ultraviolet-visible (UV-Vis) spectroscopy. The results indicated that CF-NCP has strong ability to quench the intrinsic fluorescence of BSA by forming complexes. T
PMID 24967543

in vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography.

Stępnik KE1, Malinowska I2, Rój E3.
Talanta.Talanta.2014 Dec;130:265-73. doi: 10.1016/j.talanta.2014.06.039. Epub 2014 Jun 30.
In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed. The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro dete
PMID 25159408

Molecular structure, vibrational and electronic properties of 4-Phenyl-3H-1,3-thiazol-2-ol using density functional theory and comparison of drug efficacy of keto and enol forms by QSAR analysis.

Sachan AK1, Pathak SK1, Chand S1, Srivastava R1, Prasad O1, Belaidi S2, Sinha L3.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.Spectrochim Acta A Mol Biomol Spectrosc.2014 Nov 11;132:568-81. doi: 10.1016/j.saa.2014.05.011. Epub 2014 May 15.
4-Phenyl-3H-1,3-thiazol-2-ol can exist in two tautomeric forms - keto and enol. Comprehensive investigation of molecular geometry and electronic structure in ground as well as in the first excited state of 4-Phenyl-3H-1,3-thiazol-2-ol (enol) has been carried out. To determine lowest-energy molecular
PMID 24892536

Japanese Journal

コンピュータシミュレーションによる違法ドラッグの活性予測

栗原正明
薬学雑誌 133(1), 13-16, 2013
NAID 40019555243

QSARの誕生 : Corwin Hansch先生を偲んで

藤田稔夫
Journal of pesticide science 37(2), 206-214, 2012-05-20
NAID 110009457020

QSARの誕生 : Corwin Hansch 先生を偲んで

藤田稔夫
Journal of pesticide science 37(2), 206-214, 2012-05-20
NAID 10030863819

Related Pictures

★リンクテーブル★

リンク元	「定量的構造活性相関」「quantitative structure-activity relationship」
拡張検索	「3D-QSAR」「QSART」
関連記事	「QS」