The document discusses Quantitative Structure-Activity Relationship (QSAR) in drug design, highlighting its aim to mathematically correlate biological activity with molecular structure. It covers objectives, steps in QSAR processes such as descriptor generation and model validation, as well as various analysis methods like Hansch and Free Wilson approaches. The document also outlines the advantages and disadvantages of QSAR, along with its applications in predicting activity and optimizing lead compounds.

1. Presented By Deshmukh Md Faizan
M. Pharm (2nd Sem)
DEPARTMENT OF PHARMACEUTICAL CHEMISTRY,
R. C. PATEL INSTITUTE OF PHARMACEUTICAL
EDUCATION AND RESEARCH, SHIRPUR
QSAR
Compounds + biological activity
New compounds with
improved biological activity
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2. CONTENTS
 Introduction
 Objectives
 Steps in QSAR
 Hansch Analysis
 Free Wilson Approach
 Mixed Approach
 Advantages of QSAR
 Disadvantages of QSAR
 Application of QSAR
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3. INTRODUCTION
QSAR is an attempt to remove the element of luck from drug design
by establishing a mathematical relationship in the form of an equation
between biological activity and measurable physicochemical
parameters.
QSAR is mathematical relationship between a biological activity of a
molecular system and its geometric and chemical characteristics.
QSAR(Quantitative Structure Activity Relationship) are based on the
assumption that the structure of molecule (i.e. its geometric, steric and
electronic properties) must contain the features responsible for its
physical, chemical and biological properties.
QSAR in simplest terms, is a method for building computational or
mathematical models which attempts to find a statistically significant
correlation between structure and function.
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4. OBJECTIVE
 QSAR makes it easy now to reach the conclusion for any of
the congener that still not in process, in way that whether it
will optimal and profitable or not.
 To predict the biological activities of untested and
sometimes yet unavailable compounds.
 To optimize the existing leads so as to improve their
biological activities.
 Quantitative relationship between the structure and
physiochemical properties of substances and their
biological activity are being used as the foundation stone in
search of new medicines
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5. STEPS IN QSAR
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6. 1. STEPS IN QSAR (STRUCTURE ENTRY AND
MODELLING )
Structure are sketched using standard drawing software's
commercial or freeware
Molecular modelling for the generation of low energy
conformation
Ab intitio-very small molecule, Highly
accurate, High computational costs.
Software- Gaussian.
Semi-empirical-Medium sized
molecules, accurate but computationally
intensive.
Software- MOPAC.
Molecular Mechanics- No restriction
on size, accurate with proper conformational
analysis. Software- CVFF.
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7. 2.STEPS IN QSAR (DESCRIPTOR GENERATION)
Physico-chemical properties that describe some aspect of
the chemical structure
Empirical Descriptors:-
determined experimentally NMR chemical shift,
Melting point.
Some of the experimentally determined physico-
chemical parameters:
log P = C or g/ C aqu
π x =log P (R-X) – log P (R-H)
Theoretical Descriptors:-
Calculated (theoretical) topological ,BCUT.
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8. 0D Descriptors:
(i.e. Constitutional Descriptor) molecular weight, no.of
atoms, no of non-H atoms, no. of bonds, no. of heteroatom's,
no. of multiple bonds (nMB), no. of rings, no. of circuits,
no.of H-bond donors, no. of h-bond acceptors, no. of
Nitrogen atoms (nN). Number of certain chemical groups and
functionalities in the molecule. Total no of bonds in the
molecule. Number of rings, no of rings divided by the total
no of atoms.
1D Descriptors:
include structural fragments, hydrophobicity and MR.
1. Hydrophobic Parameters are π and log P.
2. Molar Refractivity (MR)
Determined from the refractive index, the molecular
weight, and density of crystal.
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9. 2D Descriptors:
Topological Descriptors based on graph theory concepts.
These descriptors have been widely used in QSAR studies.
They help to differentiate the molecules according mostly to
their size.
Isopentane
Graph
representation
Node
Edges
Graph
Invariants(topol
ogical
descriptors)
Isopentane = Four edges, Five nodes and the adjacency
Relationship implicitly in the structure
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10. 3D Descriptor:
Geometrical Descriptor-
This descriptors using the atomic coordinates (x,y,z) of a
molecules are therefore called 3D descriptors.
Encode the 3D aspect of the structure, Vander Waals
volume, Molecular Surface.
Shadow areas, solvent accessible areas, etc.
Quantum Mechanical Descriptor-
Encode the aspect of the structures that are related to the
electrons.
Electronic descriptor include, HOMO or LUMO
Energies, partial atomic charges, etc.
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11. Comparative Molecular Field Analysis (CoMFA)
 The comparative molecular field analysis a grid based
technique, most widely used tools for three dimensional
structure-activity relationship study.
 In this method the molecule-receptor interaction is
represented by the steric and electrostatic field exerted
by each molecule.
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12. 3.STEPS IN QSAR (FEATURE SELECTION AND
MODELLING)
Traditional techniques in feature selection
Multiple Linear Regression Analysis (MLR):
This is computerised method which is correlate the biological activity
with physico-chemical properties. This method check the impact of each
variable (physico-chemical properties) on biological activity.
Principal Component Analysis (PCA):
This is also computerised method, this method is superior than MLR
since, MLR can correlate 5 times of physico-chemical properties than
compounds, but this method PLA correlate all the physico-chemical
properties with biological activity even these physico-chemical
properties are more than the number of compounds. 12

13. Partial Least Square (PLS):
Modification of the PCA technique, where the dependent
variables are also extracted into a new component as to
maximize the correlation with the extracted component, Has an
additional advantages of modelling multiple dependent
parameters.
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14. 4.STEPS IN QSAR (MODEL VALIDATION)
Selection and Validation of QSAR models
The selection and validation of the QSAR model for virtual
screening is of almost importance and should confer to the
following recommendations-
 ƒCareful selection of independent variables
 ƒSignificance of the variables (Statistical parameters)
 Minimum number of compounds per variable
 ƒImportance of the model that corroborates with known
biophysical data.
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15. HANSCH ANALYSIS
Hansch proposal that drug action could be divided into two
stages:
1. the transport of the drug to its site of action;
2. the binding of the drug to the target site.
Each of these stages is dependent on the chemical and
physical properties of the drug and its target site.
log 1/C = k1(partition parameter) + k2(electronic parameter)
. + k3(steric parameter)+ k4
where C is the minimum concentration required to cause a
specific biological response.
Hansch equations often takes the general form:
log 1/C = k1P - k2P2 + k3s + k4ES + k5
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16. LIMITATION OF HANSCH ANALYSIS
 A large number of compounds is required.
 Lead optimization technique, not a lead discovery
technique.
 Drug Receptor interactions cant be studied.
FREE WILSON APPROACH
 Related biological activity to the presence/absence of a
specific functional group at a specific location on the
parent molecule.
 It is the only numerical method which directly relates
structural features with biological properties, in contrast
to Hansch analysis where the physicochemical properties
are corrrelated with biological activity values.
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17. Log BA= contribution of unsubstituted parent compound +
contribution of corresponding substituent
B.A = µ + ∑ aij
Where,
i = number of position at which substitution occurs.
j = number of the substituent at that position.
μ = overall average.
ADVANTAGES OF FREE WILSON APPROACH
 Simple, fast and cheap method where no substitutions
constants like pi, sigma, Es etc. required.
 Greater the complexity of structure, large is the number of
possible substituents at desired positions. Hence efficiency
is high.
 Contribution of each substituent can clearly be identified.
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18. DISADVANTAGE
 A large number of analogues need to be synthesised to
represent each different substituent and each different
position of a substituent.
 It is difficult to rationalise why specific substituents are good
or bad for activity.
MIXED APPROACH
“Kubinyi has presents the combination of Hansch and Free
Wilson models as mixed approach”.
log 1C = k1π + k2σ + k3Fs + k Hansch approach
log 1C = π + ∑aij Free-Wilson approach
The mixed approach can be written as;
log 1C =∑aij + ∑ kjΦj + k 18

19. ADVANTAGES OF QSAR
 Quantifying the relationship between structure and
activity provides an understanding of the effect of
structure on activity.
 It is also possible to make predictions leading to
synthesis of novel analogues.
 The results can be used to help understand interaction
between functional groups in the molecules of greatest
activity with those of their target
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20. DISADVANTAGE OF QSAR
 False correlation may arise because biological data that
are subject to considerable experimental error.
 If training dataset is not large enough, the data collected
may not reflect the complete property space.
consequently, many QSAR results cannot be used to
confidently predict the ,most likely compounds of best
activity.
 There are many successful applications but do not expect
QSAR works all time. Also be aware of overfiting
problem.
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21. APPLICATION OF QSAR
 Prediction of activity.
 Prediction of toxicity.
 Lead compound optimization.
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22. REFERENCE
1. Graham L. Patrick, 2001. An Introduction to Medicinal Chemistry,
Oxford University Press, Page no.258-270.
2. Ashutosh Kar. 2005. Medicinal Chemistry, New Age International
Publishers, Page no.19-33
3. Dr. V. M. Kulkarni, Dr. K.G. Bothara, 2006. Drug Design, Nirali
Prakashan, Page no.8.25-8.27.
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23. THANK YOU
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