Quantitative Structure Activity Relationship

1. Tanushree Karmakar
M.Pharm (Pharmacology) 1st Year
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Dr. B.C Roy College of Pharmacy and A.H.S

2. Contents:
 Process of Drug Discovery
 Drug Designing
 Strategies of structure based drug design
 Concept of Docking
 QSAR and Drug Designing
 QSAR Steps
 Descriptors used in QSAR
 De novo drug design
 QSAR model Validation and statistical analysis
 2D-QSAR and 3D-QSAR methods
 Applications of QSAR
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3. Process of Drug Discovery
 The process of modern drug discovery starts with the identification of disease and
therapeutic target of interest , include phases , methodologies ,lead identification ,
characterization , formulation , pharmacological studies , PK profile , safety and
clinical studies.
 General steps:
 Target Selection or discovery
 Lead discovery : Lead generation and Optimization
 In vitro Studies
 Pre-clinical and clinical studies.
 A drug can be discovered from following approaches:
 From natural sources
 Screening
 Chemical modification of known drugs
 Observation of side effects
 Rational
 Serendipity
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4. Drug Designing
 Also referred as Rational drug design.
 Inventive process of finding new medications or interventions based on the
knowledge of biological target.
 More focussed approach that uses structural information about the drug receptor
or targets on one of its ligands as a basis to design , identify or create leads.
 Types of Structure based drug design:
 Receptor based drug design
 Ligand based drug design
 Factors Governing Drug design:
 Relationships between physico-chemical features and biological properties that
need to be established retrospectively.
 Quantitative structure-activity relationships (QSARs).
 Disease etiologies and various biochemical processes involved.
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5. Strategies Of Structure Based Drug Design
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Pharmacophore
Identification
Pharmacophore
Modification
Fit for the
receptor
Potential Drug
Yes
No
Active site Identification
Ligand fragments growing
Fit for the
receptor
Complete
Growing
Potential Drug
Change Fragment
No
Yes
Yes
No

6. Concept of Docking
 Docking refers to the ability to position a ligand in the active or a
designated site of a protein and calculate the specific binding affinities
and conformations at a receptor site .
 Attempts to find the “best” matching between two molecules.
 It includes finding the Right Key for the Lock .
 Software for Docking: DOCK, AUTODOCK,AUTODOCK Vina.
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https://en.wikipedia.org/wiki/Docking_(molecular)

7. Main tasks of docking tools:
Sampling of conformational (ligand) space.
Scoring protein-ligand complexes
 Molecular Docking involves:
 Identification of the ligand’s correct binding geometry (pose) in the
binding site (Binding Mode)
 Molecular Docking Prediction of the binding affinity (Scoring
Function)
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https://www.intechopen.com/books/protein-engineering-technology-and-application/protein-protein-and-protein-ligand-docking

8. QSAR and Drug designing
 Attempts to correlate structural, chemical, and physical properties with
biological activity by providing scientific credible tools for predicting and
classifying biological activities of untested chemicals.
 Involves the derivation of mathematical formula which relates the biological
activities of a group of compounds to their measurable physicochemical
parameters.
 Depends on the theory of Lipinski Rule of Five: Drug Likeliness
Screening: Method for evaluating the drug-like properties of a compound.
 Rule of five (RO5) is a rule of thumb to evaluate drug likeness or determine
if a chemical compound with a certain pharmacological or biological activity
has properties that would make it a active drug .
 QSAR’s general mathematical form is represented by the following equation:
Biological Activity = f (Physicochemical Property)
-Activity is expressed as log(1/c). C is the minimum concentration required to
cause a defined biological response.
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9.  For a compound i , the linear equation that relates
molecular properties, x1, x2 .., xn to the desired activity, y
is :
yi= xi1b1+xi2b2+………….+xinbn+ei
 Expressing the previous equation in a compact form for
the general case of n selected descriptors, the QSAR
equation results into:
yi=∑nxibi+ei
Where, b’s are linear slope that express the correlation of
particular molecular property xi with the activity yi of the
compound i ; and ei is a constant.
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10. QSAR steps:
General stages of QSAR model Development:
1. Preparing molecules for QSAR study.
2. Collection, design and calculation of values for all descriptors for all ligands
in training sets.
3. Selecting descriptors that can properly relate chemical structure to biological
activities.
4. Creating model using training set : Quantitative description of structural
variation and choice of the QSAR model .
5. Applying statistical methods that correlate changes in structure with changes
in biological activity.
6. Synthesis and Biological testing .
7. Data analysis and Validation of the QSAR models (Internal and External).
8. Interpretation of results for the proposal of new compounds : Based on
statistical experimental design and multivariate data analysis.
 Obtaining a good quality QSAR model with the ability to predict activity of
a chemical outside the training set depends upon many factors in the
approach and execution of each individual steps.
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11. Descriptors/Parameters used in QSAR
 Measure of the potential contribution of its group to a particular property of
the parent drug.
 Numerical representation of chemical information encoded within a
molecular structure via mathematical procedure.
 The information content of structure descriptors depends on two major
factors:
(1) The molecular representation of compounds.
(2) The algorithm which is used for the calculation of the descriptor.
 The three major types of parameters initially suggested are :
(1) Hydrophobic : Partition coefficient (log P) ; Hansch’s substitution
constant (π )
(2) Electronic : Hammett constant ( σ, σ +, σ ) ; Taft’s inductive (polar)
constant ( σ*)
(3) Steric : Taft’s steric parameter (Es) ; Molar volume (MV)
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12. Various types of Descriptors:
Constitutional descriptors
Geometrical descriptors
Charge descriptors
Topological descriptors
Polarizable parameters
Molecular descriptors
Connectivity indices
Functional group counts
Information indices
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13. Lipophilicity or Hydrophobicity
 It determines the
ability of the drug
molecule to cross the
biological membrane.
 More the lipophilicity,
more will be the
biological activity.
 Also important in
determining the
receptor interactions.
Partition Coefficient
 The hydrophobic character of a
drug can be measured
experimentally by testing the
drug’s relative distribution in n-
octanol /water system.
 This relative distribution is termed
as partition coefficient.
 P = [drug]in n -octanol
[drug]in aqueous system
 Hydrophobic compounds have
high P value whereas hydrophilic
compounds have a low P value.
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14.  Typically over a small range of log P , a straight line is
obtained :
log1/C= k1(log P)+k2
 If graph is extended to very high log P values, then we get
a parabolic curve:
log1/C=-k1(log P)^2+k2logP+k3
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15. Substituent hydrophobicity
constant
 It is a measure of how hydrophobic a
substituent is in relative to hydrogen
which is calculated experimentally
for a standard compound such as
benzene with or without substituent
X.
π x= log Px-log PH
Where π x is the hydrophobicity
constant, Px is the partition coefficient
for the standard compound with the
substituent , PH is the partition
coefficient of the standard compound.
Steric Factors
 Steric substitution constant : It is
a measure of the bulkiness of the
group it represents and it effects
on the closeness of contact
between the drug and receptor
site. It is much difficult to
quantify.
 Namely :
1. Taft’s steric factor (Es)
2. Molar refractivity (MR)
3. Verloop sterimol parameter
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16. Electronic Effects
 Useful to measure the electronic effect of a substituent
 Given by Hammett substitution constant: Measure of electron
withdrawing or electron donating ability of a substituent and is
determined by measuring the dissociation of a series of
substituted benzoic acid compared to the undissociated benzoic
acid itself.
 Hammett constant takes into account both resonance and
inductive effects; thus, the value depends on whether the
substituent is para or meta substituted.
-ortho position not measured due to steric effects.
σx= log (Kx/K-benzoic acid)
 Where σx is the Hammett constant , Kx is the dissociation
constant of substituted benzoic acid.
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17. Hansch Analysis
 Proposed that drug action could
be divided into 2 stages:
1) Transport of drug to site &
2) Binding of drug to site
 Each of these stages depend
upon the physical and chemical
properties of the drug.
 It attempts to mathematically
relate drug activity to
measurable chemical property.
 Log 1/C = k1(partition
parameter) + k2(electronic
parameter)+ k3(steric
parameter) + k4
Free Wilson Approach
 This method is based on the
assumption that the introduction of
a particular substituent at a
particular molecular position ,
always leads to a quantitatively
similar effect on biological potency
of the whole molecules and
expressed by the equation as
 BA= μ+Σaj
 For a series of chemical analogs ,
the biological activity is assumed to
be the sum of intrinsic activity of
the skeleton (μ) and the additive
contribution of the substituents (aj).
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18. De novo drug design
 De novo means starting from the beginning.
 Offers a broader exploration of chemical space and therefore makes it
possible to identify novel ligand scaffolds.
 Design of novel chemical structures capable of interacting receptors
with known structures.
 Approach to build a customized Ligand for a given receptor, involving
ligand optimization.
 Ligand optimization can be done by analyzing protein active site
properties that could be probable area of contact by the ligand using
molecular modeling tools.
 Types of de novo drug design :
1. Manual design
2. Automated design : Revolves around the scoring functions used to
estimate binding affinities .It is prone to generating structures which
are either difficult or impossible to synthesize.
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 De novo design Classes of design methods:
1. Methods that analyze active site
2. Methods that dock whole molecule
3. Methods that connect molecular fragments or atoms together to produce a
ligand:
 Site- point connection methods
 Fragment connection methods
 Sequential build up methods
 Random connection methods
 Some de novo design methods are :
DOCK,AUTODOCK,CAVEAT,GRID,LUDI,SPROUT
http://www.medicilon.com/de-novo-drug-design/

20. Methods for validating
QSAR models:
 Internal validation :
1. Least Squares Fit
2. Fit of the Model
3. Cross-validation
4. Bootstrapping
5. Randomization test (Y-
Scrambling model)
 External validation
Statistical analysis methods for
predicting QSAR model :
 Regression Analysis
 Principle Component Analysis
 Partial Least square Analysis
 Clustered Analysis:
1. Hierchial Clustering
2. K-nearest neighboring method of
clustering
3. Artificial neuronal network
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21. 2D-QSAR Methods:
1. Free Energy Models : Hansch
Analysis
2. Mathematical Models :Free
Wilson Analysis, Fujita Ban
Modification
3. Other Statistical methods :
Discriminant Analysis ,
Principle component Analysis ,
Cluster Analysis , Combine
Multivariate Analysis , Factor
Analysis
4. Pattern Recognition
5. Topological Methods
6. Quantum Mechanical Method
3D - QSAR Methods:
1. Molecular shape analysis
(MSA)
2. Molecular topological
difference (MTD)
3. Comparative molecular
movement analysis (COMMA)
4. Hypothetical Active Site
Lattice (HASL)
5. Self Organizing Molecular
Field Analysis (SOMFA)
6. Comparative Molecular Field
Analysis (COMFA)
7. Comparative Molecular
Similarity Indices (COMSIA)
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22. Applications of QSAR
Rational identification of new leads with
pharmacological or biocidal activity.
Identification of hazardous compounds at early
stages of product development.
Designing out of toxicity and side effects in
new compounds.
Prediction of variety of physio-chemical
properties of molecules.
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23. References:
 Medicinal Chemistry by Ashutosh Kar,Fourth Edition.
 QSAR: Hansch Analysis and Related Approaches by Hugo
kubiany,VCH 1993.
 A Review on Computational Methods in Developing Quantitative
Structure-Activity Relationship (QSAR);International Journal of Drug
Design and Discovery :Volume 3 • Issue 3 • July – September 2012.
815-836.
 Validation of QSAR Models - Strategies and Importance ; International
Journal of Drug Design and Discovery: Volume 2, Issue 3 ,July –
September 2011. 511-519
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