Qsar studies

Department of Pharmaceutical chemistry
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QUANTITATIVE STRUCTURAL ACTIVITY
RELATIONSHIP (QSAR)
HINDU COLLEGE OF PHARMACY
GUNTUR.
Presented by:
R.Aruna sri
Y17MPH0222

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INTRODUCTION: 1
Medicinal chemists have tried to quantify relationships between chemical
structure and biological activity since before the turn of the century.
However, it was not until the early 1960s, through the joint effect of Crowin
Hansch and his computer that, a workable methodology developed known as
Quantitative structural activity relationship (QSAR).
In 1968 Crum-Brown and Fraser published an equation which is
considered to the first general formulation of QSARs. In their investigation
on different alkaloids they recognized that alkylation of the basic nitrogen
atom produced different biological effects of the resulting quaternary
ammonium compound, when compared to the basic amines. Therefore they
assumed that, biological activity must be the function of the chemical
structure. BA = f [C] …………1
Richet discovered that toxicity of organic compounds inversely follows their
water solubility. Such relationship shows that changing the biological activity
(ΔBA) corresponds to the change in the chemical and physiochemical
properties ΔC. ΔBA = f [ΔC] …………2
All the QSAR equation corresponds to equation 2, because only the
difference in BA are quantitatively correlates with changes in Lipophilicity
and or other physicochemical properties of the compound under
investigation.
Meyer and Overtone Fuhrer suggested a linear relationship between
Lipophilicity and narcotic activities. Fuhrer realized that within homologous
series narcotic activity increases in a geometric progression which proves
that additivity of group contributes to biological value.
Ferguson gave an interpretation of nonlinear structure activity relationship
with thermodynamics, which also explain the ‘‘cut off ’’ of biological
activity beyond certain range of biological activities.
Additional development of QSAR occurred until the work of Louis
Hammett (1894-1987) who correlated electronic properties of organic acid
and bases with equilibrium constant and reactivity. Hammett sigma value are
often used for electronic parameters, but quantum mechanically derived
electronic parameters may also be used.

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QSAR involves the deviation of mathematical formula which relates the
biological activities of a group of compounds to their measurable
physiochemical parameters. These parameters have major influence on the
drug’s activity. QSAR derived equation take the general form:
Biological activity = function {parameters}
In which activity is expressed as log (1/C). Where C is the minimum
concentration required to cause a defined biological response. QSAR based
on Hammett’s relationship utilizes electronic properties as the descriptors of
structures.
Hansch recognized the importance of Lipophilicity, expressed as octanol-
water partition coefficient, on biological activity. This parameter provides a
measure bio-availability to compounds, which will determine, in part the
amount of compounds that gets to the target site.
All these reveal that biological activity of a drug is a function of chemical
features (i.e. Lipophilicity, electronic and steric) of the substituents and
skeleton of the molecule.
For example Lipophilicity is the main factor governing transport,
distribution and metabolism of drug in biological system.
Similarly electronic and steric features influence the metabolism and
pharmacodynamics process of the drug.
A major problem in QSAR studies arise because hydrophobic, electronic and
steric affect overlaps and cannot be neatly separated.
Definition:
Quantitative structure-activity relationships are mathematical relationships linking
chemical structure and pharmacological activity in a quantitative manner for a
series of compounds. Methods which can be used in QSAR include various
regression and pattern recognition techniques.
Classical QSAR analyses: (Hansch- and Free Wilson analyses) consider only 2D
structures. Their main field of application is in substituent variation of a common
scaffold.
3D-QSAR analysis: (CoMFA) has a much broader scope. It starts from 3D
structures and correlates biological activities with 3D-property fields.

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Parameters:
 The QSAR approach uses parameters which have been assigned to the
various chemical groups that can be used to modify the structure of the drug.
 The parameter is a measure of the potential contribution of its group to a
particular property of the parent drug.
 The various parameters used in QSAR studies are:
Parameters Type of parameters
Lipophilic parameters Partition coefficient, Chromatographic
parameters and Substitution constant
Polarizability parameters Molar refractivity, Molar volume,
parachor
Electronic parameters
Hammett constant, field and
resonance parameters, dipole
moment, quantum – chemical
parameters, charge – transfer
constant.
Steric parameters Taft’s steric constant, Vander Waals
radii
Miscellaneous parameters
Molecular weight, Geometric
parameter, conformational entropies,
connectivity indices, other topological
parameters.
Lipophilic parameters:
 Lipophilicity is defined by the partitioning of a compound between an
aqueous and a non-aqueous phase. Two parameters are commonly used to
represent Lipophilicity, namely the partition coefficient (p) and lipophilic
substituent constant (π).
Partition coefficient:
 A drug has to pass through a number of biological membranes in order to
reach its site of action. Consequently, organic / aqueous system partition
coefficient is the obvious parameters used to measure the movement of drug
through these members.

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 Partition coefficient is generally given as:
P = [C] org. / [C] aqu.
It is a ratio of concentration of substance in organic and aqueous phase of a
two compartment system under equilibrium conditions.
For easily ionisable drug, correlation must be made as follows :
P = [C] org. / [C] aqu.(1-)
Where  = degree of ionization
 The accuracy of the correlation of drug activity with partition coefficient
will depend on the solvent system used as a model for the membrane.
Aqueous medium: pure water and buffer solution
Organic medium: n-octanol – More consistent result for absorbed in GI.
Olive oil - More consistent result correlation for crossing BBB.
Chloroform - More consistent value for buccal absorption.
 n- octanol / water system is frequently chosen, because it appears to be good
mimic of lipid polarity; however more accurate results may be obtained if
the organic phase is matched to the area of biological activity being studied.
Advantages:
 It is a low vapor pressure, allowing reproducible measurements.
 It is UV transparent over a large range, making the quantitative
determination of a compound is relatively easy.
The nature of the relationship between P and drug activity depends on the
range of P value obtained in the compounds used.
If this range is small the result may be expressed as a straight line equation
having the general form:
Log 1/C = K1 log P + K2
 This equation indicates a linear relationship between the activity of the
drug and its partition coefficient.
 If the large range of P values the graph of log 1/C against log P often has
a parabolic form with a maximum value (log Po
).

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 The existence of this maximum value implies that there is an optimum
balance between aqueous and lipid solubility for maximum biological
activity.
 Below Po
the drug - Reluctant enter the membrane
 Above Po
the drug - Reluctant to leave the membrane
The parabolic relationship could be represented by equation of the form:
Log (1/C) = -K1 (log P)2
+ K2 log P + K3
Chromatographic parameters:
 When the solubility of a solute is considerably greater in one phase than the
other, partition coefficient becomes difficult to determine experimentally.
 Chromatographic parameters obtained from reversed phase thin layer
chromatography are occasionally used as substitute for partition coefficient.
 Silica gel plate, being coated with hydrophobic phases, is eluted with
aqueous/ organic solvent system of increasing water content.
 The Rf values are converted into Rm value, which are the true measure of
Lipophilicity from the following equation:
Rm value has been used as a substitute for partition coefficient in QSAR
investigation.
Advantages:
To determine the Rm values offers many important advantages, as
compared to the measure of log P values:
 Compounds need not be pure
 Only trace of materials needed
 A wide range of hydrophilic and lipophilic congeners can be
investigated.
 The measurement of practically insoluble analogs possesses no
problem
 No quantitative method for concentration determination
needed.
 Several compounds can be estimated simultaneously.
Disadvantages:
 Lack of precision and reproducibility
Rm = log (1/Rf - 1)

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 Use of different organic solvent system renders the deviation
of π and f related scales impossible.
π- Substituent constant/ lipophilic substitute constant:
Lipophilic substituent constants are also known as hydrophobic substituent
constants (π).
The π – substituent constant defined by Hansch and co-workers by the
following equation:
π = log PRH - log PRX
PRH and PRX = Partition coefficient of the standard and its mono-
substituted derivative.
Substituents π meta π para π benzene
-H 0.00 0.00 0.00
-CH3 0.51 0.52 0.56
-Cl 0.76 0.70 0.71
-Br 0.94 1.02 0.86
-OH -0.49 -0.61 -0.67
-OCH3 0.12 -0.04 -0.02
-NO2 0.11 0.24 -0.28
A positive π value indicates that the π-substituent has a higher Lipophilicity
than hydrogen and the drug favours the organic phase.
A negative π value indicates that the π-substituent has a lower Lipophilicity
than hydrogen and the drug favours the aqueous phase.
E.g.
(Log P = 2.13)
Benzene
(Log P = 2.84)
Chloro benzene
Cl
(Log P = 0.64)
Benzamide
CONH2
(π Cl = 0.71) (πCONH2 = -1.49)

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 Positive values imply substituent are more hydrophobic than H..
 Negative values imply substituent’s are less hydrophobic than H.
 A QSAR equation may include both P and π.
 P measures the importance of a molecule’s overall hydrophobicity
(Relevant to absorption binding etc.)
 P identifies specific regions of the molecule which might interact with
hydrophobic regions in the binding site.
 The application of π value for liophilicity calculation of aliphatic compound
led to significant deviation between observed and calculated values.
For example, from the definition of π value, πH must be zero and no
difference between πCH3 and π CH2 .
But the lipophilic contribution of hydrogen atom is not zero.
Hence Rekker suggested a new system, known as hydrophobic
fragmentation constant, which is measure of the absolute liophilicity
contribution of the corresponding substituent or group and is no longer based
on the exchange of H for X, as π values:
Log P = Σai fi
Σai = No. of occurrences of the fragments with liophilicity contribution
fi = Hydrophobic fragmentation constant
Polarizability parameters
Molar refractivity:
The molar refractivity is a measure of both the volume of a compound
and below easily it is polarized.
n = refractive index; M = Molecular weight; d = density.
MR = (n2
-1)M
(n2
+ 1)d

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 The term Mw/d defines a volume, while the term (n2
- 1)/ (n2
+ 2) provide a
correction factor by defining how easily the substituent can be polarized.
 This is particularly significant if the substituent has a π electron or lone pair
of electrons.
Significance:
 Molar refractivity terms in QSAR equation of some ligand- enzyme
interaction could be interpreted with the help of 3D structure.
 These investigation shows that substituent modeled by MR bind in polar
areas, while substituents modeled by π, bind in hydrophobic space.
 The positive sign of MR in QSAR equation explains that the substituent
binds to polar surface, while a negative sign or non – linear relationship
indicates steric hindrance at the binding site.
Parachor:
The parachor [p] is molar volume V which has been corrected for forces of
intermolecular attraction by multiplying the fourth root of surface tension.
It is expressed mathematically as
M= molecular weight; D= density.
Electronic parameters:
 The distribution of electron in a drug molecule has a considerable influence
on the distribution and activity of the drug.
 In general, non-polar and polar drug in their unionized form are more readily
transported through membranes than polar drugs and drugs in their ionized
form.
 If the drug reaches the target site, the distributed electron will control the
type of bond that it forms with the target site, which in turn affects its
biological activity.
 The first attempt to quantify the electronic effect of groups on the
physiochemical properties of compounds was made by Hammett.
[p] = V
1/4
= M1/4
/D

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Hammette constant:
 The distribution of electrons within a molecule depends on the nature of the
electron withdrawing and donating group found in the structure.
 Hammette used this concept to calculate what now as Hammett constant (σ)
for a variety of mono-substituted benzoic acids.
 He used these constants to calculate the equilibrium and rate constants for
chemical reactions.
 However, they are now used as electronic parameters in QSAR
relationships .
 Hammett constant are defined as :
σx = log KBX / KB
i.e. σ x = log KBX - KB
 So as pKa
= - log Ka
σx = p KB - p KBX
Where kB and KBX = Equilibrium constants for benzoic acid and mono
substituted benzoic acid respectively
 Hammett substitution constant (σ) the electron withdrawing or electron
donating ability of a substituent and has been determined by comparing the
dissociation of series of substituted acid with that of parent or un-substituted
acid.
 Negative value of σx indicates; the substituent is acting as an
electron donor group.
 Positive value of σx indicates; the substituent acting as an electron
withdrawing group.
 Hammett constant takes into account both resonance and inductive effect.
 Therefore the value of 𝜎 for a particular substituent will be depending on
whether the substituent is meta or para.
 The meta and para σ value are commonly used and indicates by a subscript
m and p after the symbol σ ortho σ are the often unreliable due to steric
hindrance and other effects such as intra molecules hydrogen bonding.

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Disadvantage:
Hammett constant suffer from the disadvantage that they only apply to
substituents directly attached to benzene ring.
Most QSAR studies start off by considering σ and if there is more than one
substituents, the values are summarized Hammette substitution has been
unsuccessful to relate biological activity since electron distribution is not the
only factor involved.
INDUCTIVE SUBSTITIUENT CONSTANT:
 Hammett constant is a measure of both inductive and mesmeric effect.
 The p- substituent constant (σp) has a greater resonance component than the
equivalent meta constant (σm) and the inductive contribution can be
calculated from the inductive substituents constant (σ1).
Uses:
 It is used in the aliphatic compound in which influencing and influenced
group do not form a part of a conjugated system.
Steric parameters:
 For a drug to interact with an enzyme or to receptor, it has to approach to the
binding site.
 The bulk, size and shape of the drug may influence on this process.
 A steric substitution constant is a measure of the bulkiness of the group it
represents and its effect on the closeness of contact between the drug and the
receptor site.
TAFT’S SUBSTITUENTS CONSTANT:
 Taft’s substituent’s constant (σ*) are a measure of the polar effect of
substituents in aliphatic compounds when the group in the question does
not form part of a conjugatated system.
σ1 = ½ (3 σp -σm)

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 They are based on the hydrolysis of ester and calculated from the
following equation:
σ* =1/2.48 [log (k/ ko)B – log (k/ko)A]
 K= rate constant for the hydrolysis of the substituted compound.
 K0 those of methyl derivatives
 B = Basic hydrolysis
 A = Acid hydrolysis.
 Factor 2.48 = Constant on to the same scale as the Hammette constant.
 Only the basic term is influenced by polar effect, so that by subtracting the
acid term from the basic term only the polar effect remain.
 In Taft’s substituent constant only methyl group is the standard for which
the constant is zero.
 However, that can be compared with other constant by writing the methyl
group in the form CH2-H and identifying it as the group for H.
σ*
= 2.51 σ1
Verloop steric parameter:
 Verloop steric parameter is called as sterimol parameter, which involves a
computer programme to calculated the steric substituent values from
standard bond angles, vander Waal’s radii, bond angle length and possible
conformation for substituents.
 It can be used to measure any substituent.
Charton’s steric constants:
 The principle problem with Vander Waal’s radii and Taft’s Es value is the
limited number of groups to which these constants have been allocated.
 Charton’s introduced a corrected Vander Waal’s radius U in which the
minimum Vander Waal’s radius of the substituent group (rv(min)) is corrected
for the corresponding radius for hydrogen (rvH), as defined by equation.
 They were shown to be a good measure of steric effect by correlation with Es
values.

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Minimal Steric Difference:
 This parameter assesses the difference between molecules in terms of the
parts which do not overlap when one chemical formula is placed on top of
the other.
 For example, piperidine is compared with pyrrolidine the methylene group,
surrounded by the dotted circle, will determined the MSD.
 Since this is the only portion which does not overlap.
 The rules of the calculation are as follows:
 Hydrogen atoms are ignored.
 Elements in the second period of the periodic table have a weighting of 1.
 Elements in the third period have a weighting of 1.5.
 Elements in higher periods have a weighting of 2.
Thus the MSD between piperidine and pyrrolidines is 1 and that between
pyrrolidine.
Molecular connectivity:
 Molecular connectivity’s, designated m x, can be employed as steric
parameters.
 The superscript m denotes the order of the parameter.
 Zero order connectivity (0
χ) is the simplest and is definition by equation:
 δ i = It is a number assigned to each non-hydrogen atom, reflecting
the number of non-hydrogen atoms bonded to it.
2D QSAR2,1
:
1. Free energy models:
a) Hansch analysis [Linear Free Energy Relationship(LFER)]
U = rv(min) – rvH -1.20
0
χ = Σ(δ i)- 1/2

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2. Mathematical models:
a) Free Wilson analysis
b) Fujita-Ban modification
3. Other statistical methods
a) Discriminant Analysis (DA)
b) Principle Component Analysis (PCA)
c) Cluster Analysis (CA)
d) Combine Multivariate Analysis (CMA)
4. Quantum mechanical methods
Hansch analysis: / Extra thermodynamic approach:
 In 1969, Corwin Hansch extends the concept of linear free energy
relationships (LFER) to describe the effectiveness of a biologically active
molecule.
 It is one of the most promising approaches to the quantification of the
interaction of drug molecules with biological system.
 It is also known as linear free energy (LFER) or extra thermodynamic
method which assumes additive effect of various substituents in electronic,
steric, hydrophobic, and dispersion data in the non-covalent interaction of a
drug and bio-macro molecules.
 This method relates the biological activity within a homologous series of
compounds to a set of theoretical molecular parameters which describe
essential properties of the drug molecules.
 Hansch proposed that the action of a drug as depending on two processes.
 From point of entry in the body to the site of action which involves
passage of series of membranes and therefore it is related to partition
coefficient log P (lipophilic) and can be explained by random walk
theory.
 Interaction with the receptor site which in turn depends on,
a) Bulk of substituent groups (steric)
b) Electron density on attachment group (electronic)

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 This is most popular mathematical approach to QASR introduced by Crowin
Hansch.
If the hydrophobicity values are limited to a small range then the equation will
be linear as follows:
log (1/C) = k1log p + k2 σ+ k3Es + k4
Where
K1, k2, k3 = constant obtained by least square procedure.
C = Molar concentration that procedure certain biological action.
 The molecule which is too hydrophilic or too lipophilic will not be able to
cross the lipophilic or hydrophilic barriers respectively.
 Therefore the p values are spread over a large range, then the equation will
be parabolic and given as:
Log(1/C) = -k(log p)2
+ k2 log p + k3σ+k4 ɛs + k5
K1-k5 = constants obtained by least square method
 Not all the parameters are necessarily significant in a QSAR model for
To derive an extra thermodynamic equation following rules are formulated by
Hansch:
 Selection of independent variables.
 A wide range of different parameters like log p, π, σ, MR, steric parameters
etc should be tried.

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 The parameters selected for the ‘best equation’ should be essentially
independent i.e. the inter correlation coefficient should not be larger than 0.6
– 0.7.
 All the reasonable parameters must be validated by appropriate statistical
procedure i.e. either by stepwise regression analysis or cross – validation.
 The “best equation” is normally one with lower standard deviation and
higher F value.
 If all the equations are equal the one should accept the simplest one.
 Number of terms or variable should be at least 5 or 6 data point per variable
to avoid chance correlations.
 It is important to have a model which is consistent with known physical-
organic and bio-medical chemistry of the process under considerations.
Applications:
It is may be used to predict the activity of an as yet un-synthesized analogue.
This enables the medicinal chemist to make a synthesis of analogue which is
worthy.
However these predictions should only be regarded as yield as valid, if they
are made within the range of parameter values used to establish the Hansch
equation.
Hansch analysis may also be used to give an indication of the importance of
the influence of parameters on the mechanism by which a drug acts.
Free Wilson analysis:
 The Free-Wilson approach is truly a structure-activity based methodology
because it incorporates the contribution made by various structural
fragments to the overall biological activity.
 “Mathematical model”, ‘additivity model’ or de-novo approach are the
synonyms for the Free-Wilson method.
 This is an alternative procedure to Hansch model, the substituent constant
based on biological activities is used rather than physical properties.
 The method is based upon an additive mathematical model in which a
particular substituent in a specific position is assumed to make an additive

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and constant contribution to biological activity of a molecule in a series of
chemically related molecule.
 This method is based on the assumption that the introduction of a particular
substituent, at a particular molecular position, always lead to a quantitatively
similar effect on biological potency of the whole molecules
 Expressed by equation as:
BA = Σ ai xi + μ
Where
 BA = biological activity.
 μ = overall activity
 ai = contribution of each structural feature,
 xi = denotes the presence (x i = 1) or absence (x i = 0) of
particular structural fragment.
 This mathematical model incorporated symmetry equation to minimize
linear dependence between variables.
Applications:
The Free – Wilson approach is approach was easy to apply.
Especially, in the early phases of structure activity analyses.
It is a simple method to derive substituent contribution and to have a first
look on their possible dependence on different physicochemical properties.
The substituent which cannot fulfil the principle of activity can be
recognized.
Substituent constants like π, σ etc, were not considered and so this method
is effective, when substituent constants are not available.
Disadvantages:
 The structural variation is necessary in at least two different position of
substitution; otherwise meaningless group contribution would result.
 Large number of parameters is needed to describe relatively few compounds.
 Only a common activity contribution can be derived for substituents which
always occur together in different position of the molecule.
 Only small number of new analogues can be predicted.

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The Fajita – Ban modification:
 Fajita – Ban, reformulated the Free- Wilson equation, where the constant
term μ is now defined as the calculated biological activity value of the un-
substituted parent compound of the series.
 Fujita and Ban proposed a simplified approach that solely focused on the
additivity of group contribution.
Log A/A0 = Σ Gi Xi
Where
 A and A0 = biological activity of the substituted and un-
substituted compounds respectively.
 Gi = Activity of the substituent,
 Xi =Value of 1 or 0 that corresponded to the presence or
absence of that substituent
Applications:
The table for regression analysis can be easily generated.
Addition and elimination of the compound is simple and does not
significantly change the values of other regression analysis.
Any compound may be chosen as the reference compound xi singularity
problems are avoided.
Mixed approach:
 Hansch analysis and Free- Wilson model differ in their application, but they
are closely related.
 Mixed approach of this with indicator variable offers the advantages of both,
Hansch analysis and Free- Wilson analysis and widens their applicability.
 Today the mixed approach is the most powerful tool for the quantitative
description of large and structurally diverse data sets.
 The mixed approach can be written as:
Log 1/C = Σ ai xi + Σ kj Øj + k
Where
kj = coefficient of different physiochemical parameters
Σ ai xi = Free- Wilson for the substituent

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Øj = π, σ and Es contribution of the parent skeleton
Statistical Methods:
 Statistical methods are the mathematical foundation for the development of
QSAR models.
 The application of multi
 variate analysis, data description, classification, and regression modelling,
are combined with the ultimate goal of interpretation and Prediction of non-
evaluated or non-synthesized compounds.
Discriminant Analysis:
 The aim of Discriminant analysis is to try and separate molecules into their
Constituent classes.
 Discriminant analysis finds a linear combination of factor that best
discriminate between different classes.
 Linear Discriminant analysis was used for the analysis rather than multiple
linear regressions since the biological activity data were not on a continuous
scale of activity but rather were classified into two groups: active and
inactive.
 It is used to obtain a qualitative association between molecular descriptor
and the biological property.
Cluster Analysis:
 Cluster analysis is the process of dividing a collection of objects (molecules)
into groups (or cluster) such that the objects within a cluster are highly
similar whereas objects in different clusters are dissimilar.
 When applied to a compound dataset, the resulting clusters provide an
overview of the range of structural types within the dataset and a diverse
subset of compounds can be selected by choosing one or more compounds
from each cluster.
 Clustering methods can be used to select diverse subset of compounds from
larger dataset.
 The clustering methods most widely applied to compound selection include
k-means clustering, non-hierarchical clustering and hierarchical clustering.
Principle Component Analysis:
 The dimensionality of a data set is the number of variables that are used to
describe each object.

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 Principle Components Analysis (PCA) is a commonly used method for
reducing the dimensionality of data set when there are significant
correlations between some or all of the descriptors.
 PCA provides a new set of variables (the principle component) which
represent most of the information contained in the independent variables.
Quantum Mechanical Methods:
 Quantum mechanical techniques are usually used to obtain accurate
molecular properties such as electrostatic potential or polarizabilities, which
are only available with much lower resolution from classical mechanical
techniques or those (ionization potential or electron affinities, etc.) that can
be obtained only quantum mechanically.
 The methods used commonly divided into three categories:
Semi-empirical molecular orbital theory
Density functional theory (DFT)
Ab-initio molecular orbital theory
 Quantum chemical methods can be applied to quantitative structure-activity
relationship by direct derivation of electronic descriptors from molecular
wave function.
 There is no single method that works best for all problems.
 Besides above mentioned methods, statistical modelling techniques aim to
develop correlation models between independent variables (molecular
descriptors) and dependent variable (biological property)
 Which include simple linear regression, multiple linear regressions, principle
component regression, partial least squares (PLS) regression, genetic
function approximation (GFA) and genetic partial least squares (G/PLS)
techniques.
3D-QSAR:
 Three-dimensional quantitative structure-activity relationships (3D-QSAR)
involve the analysis of the quantitative relationship between the biological
activity of a set of compounds and their three-dimensional properties using
statistical correlation methods.
 3D-QSAR uses probe-based sampling within a molecular lattice to
determine three-dimensional properties of molecules (particularly steric and
electrostatic values) and can then correlate these 3D descriptors with
Molecular shape analysis (MSA):

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 Molecular shape analysis wherein matrices which include common overlap
steric volume and potential energy fields between pairs of superimposed
molecules were successfully correlated to the activity of series of
compounds.
 The MSA using common volumes also provide some insight regarding the
receptor-binding site shape and size.
Molecular topological difference (MTD):
 Simons and his co-workers developed a quantitative 3D-approach, the
minimal steric (topologic) difference approach.
 Minimal topological differences use a ‘hyper molecule’ concept for
molecular alignment which correlated vertices (atoms) in the hyper molecule
(a superposed set of molecules having common vertices) to activity
differences in the series.
Comparative molecular movement analysis (COMMA):
 COMMA – a unique alignment independent approach.
 The 3D QSAR analysis utilizes a succinct set of descriptors that would
simply characterize the three dimensional information contained in the
movement descriptors of molecular mass and charge up to and inclusive of
second order.
Hypothetical Active Site Lattice (HASL):
 Inverse grid based methodology developed in 1986-88, that allow the
mathematical construction of a hypothetical active site lattice which can
model enzyme-inhibitor interaction and provides predictive structure-
activity relationship for a set of competitive inhibitors.
 Computer-assisted molecule to molecule match which makes the use of
multidimensional representation of inhibitor molecules.
 The result of such matching are used to construct a hypothetical active site
by means of a lattice of points which is capable of modelling enzyme-
inhibitor interactions.
Comparative Molecular Field Analysis (COMFA):
 The comparative molecular field analysis a grid based technique, most
widely used tools for three dimensional structure-activity relationship
studies was introduced in 1988, is based on the assumption that since, in
most cases, the drug-receptor interactions are non-covalent, the changes in
biological activities or binding affinities of sample compound correlate with
changes in the steric and electrostatic fields of these molecules.
 These field values are correlated with biological activities by partial least
square (PLS) analysis.
3D Pharmacophore modelling:

Page 22
Pharmacophore modelling is powerful method to identify new potential
drugs.
Pharmacophore models are hypothesis on the 3D arrangement of structural
properties such as hydrogen bond donor and acceptor properties,
hydrophobic groups and aromatic rings of compounds that bind to the
biological target.
The Pharmacophore concept assumes that structurally diverse molecules
bind to their receptor site in a similar way, with their pharmacophoric
elements interacting with the same functional groups of the receptor.
Bibliography:
1. K.Ilango, P.Valentina, Medicinal chemistry,keerthi
Publishers(Chennai),Pg.No: 1-25
2. http://shodhganga.inflibnet.ac.in/bitstream/10603/27948/14/14_chapter8.pdf

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