Quantitative Structure Activity Relationship (QSAR)

Quantitative Structure Activity
Relationship (QSAR)
Abhijit Debnath
Asst. Professor
NIET, Pharmacy Institute
Friday, June 11, 2021
1
Abhijit Debnath | BP807ET-CADD | Unit-2
Noida Institute of Engineering and Technology
(Pharmacy Institute) Greater Noida
Unit: 2
Subject Name: CADD (Elective)
(BP 807 ET)
Course Details
(B. Pharm 8th Sem)

Friday, June 11, 2021 2
SYLLABUS

Friday, June 11, 2021 3
CONTENT
• SAR versus QSAR, History and development of QSAR,
• Types of physicochemical parameters, experimental and theoretical approaches for the
determination of physicochemical parameters such as Partition coefficient, Hammet’s
substituent constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.

Objectives: Upon completion of the subject student shall be able to;
1. The concept of QSAR
2. Applications of QSAR
Friday, June 11, 2021
4
COURSE OBJECTIVE

Friday, June 11, 2021 Abhijit Debnath | BP807ET-CADD | Unit-2 5
COURSE OUTCOME (CO)
CO Statement Domain Bloom’s level
CO2.1 Apply the principle of QSAR in Drug Discovery. Cognitive L3
• After completion of this unit it is expected that students will be able to

PROGRAM OUTCOMES (POs)
PO 1 Pharmacy Knowledge
PO 2 Planning Abilities
PO 3 Problem analysis
PO 4 Modern tool usage
PO 5 Leadership skills
PO 6 Professional Identity
PO 7 Pharmaceutical Ethics
PO 8 Communication
PO 9 The Pharmacist and society
PO 10 Environment and sustainability
PO 11 Life-long learning

CO-PO MAPPING
COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11
CO2.1
3 3 3 2 3 3 3 2 3 2 3

TOPIC OBJECTIVE
• Learning the basics of QSAR to optimized lead molecules in Drug Discovery Process.
8

Unit Topics Mapping with CO2.1
Unit : 2
Quantitative Structure Activity
Relationship (QSAR)
SAR 3
QSAR 3
1D/2D/3D/4D/5D/6D QSAR 3
Course Outcome
Topic Mapping with Course Outcome
• After completion of this unit it is expected that students will be able to

Thursday, May 13, 2021 Abhijit Debnath | BP807ET-CADD | Unit-1 1
0
TOPIC OBJECTIVE MAPPING WITH COURSE OUTCOME
Topics Topic Objective Mapping with CO
SAR
To know about the Structural Activity with
biological activity
CO2.1
QSAR
To learn and apply the Mathematical relationship
of structure with the biological functions of a
molecule
CO2.1
1D/2D/3D/4D/5D/6D QSAR
To Learn and develop QSAR Models for lead
optimization.
CO2.1

• Students must have basic knowledge of Biochemistry and Medicinal Chemistry
• Students must have basic knowledge of genetic engineering, medicine and fermentation technology.
• Students must have basic knowledge of SAR.
PREREQUISITE AND RECAP

SAR Vs QSAR
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SAR Vs QSAR
 Definition
 Differences
 SAR
 QSAR
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SAR and QSAR
Definitions
CO2.1
SAR (Structure activity relationship)
• The analysis of the dependence of biological effects of a chemical upon its
molecular structure.
• Molecular structure and biological activity are correlated by observing the results
of systematic structural modification on defined biological endpoints.
QSAR (Quantitative structure activity relationship)
• A QSAR is a mathematical relationship between a biological activity of a molecular
system and its geometric and chemical characteristics.

SAR
• SAR relationship between the chemical or 3D
structure of a molecular and its biological activities
QSAR
• QSAR is a mathematical relationship between the
biological activities and measurable
physiochemical parameter
• It is used to develop a new drug that has
increased activity
• Its mainly help in drug designing purpose
• SAR is mainly done by lead molecule • QSAR optimize the properties of a lead compound
• SAR is not special case of qsar • QSAR is a special case of sar
• In SAR we dont consider the amount of the change
of the potency
• QSAR dealing with all the potency and change
potency or efficacy
Differences between SAR and QSAR
CO1
CO2.1
CO2.1

Introduction to Structural Activity Relationship (SAR)CO1
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 Structure determines function!
 Presented by Crum-Brown and Fraser in 1865
 The structure of a compound determines the
biological effects it has?
 Determination of structure/function relationships
helps medicinal chemists synthesize new drugs
and pharmaceutical agents.

Introduction to Quantitative Structure Activity Relationship (SAR)
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 QSAR: Quantifying the relationship between physicochemical properties and biological activity.
 Aim - Identify which functional groups are important for binding and/or activity

Introduction to Quantitative Structure Activity Relationship (QSAR)
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 Advantages of QSAR studies:
QSAR enables calculation, in advance, what the biological activity of a novel analog might be, thus cutting down
the number of analogs which have to be made. It allows the medicinal chemist some level of prediction.

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 Method
• Alter, remove or mask a functional group
• Test the analogue for activity
• Modifications may disrupt binding by electronic / steric effects
• Easiest analogues to make are those made from lead compound
• Possible modifications may depend on other groups present
• Some analogues may have to be made by a full synthesis (e.g. replacing an aromatic ring with a cyclohexane ring)
• Allows identification of important groups involved in binding

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2D analysis –
In this two types
 Hansch analysis
 Free-Wilson Analysis
3D analysis-
 Comparative Molecular Field Analysis (COMFA) - It provide
Steric values, Electrostatic values
 Comparative Molecular Similarity Index Analysis (CoMSIA)
It provide steric and electrostatic values and conside: H -
bond donor, H - bond acceptor, Hydrophobic field

History of QSAR
CO1
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Dmitry Mendeleév
(1834 – 1907)
• Russian chemist who arranged the 63 known elements into a periodic table
based on atomic mass, which he published in Principles of Chemistry in 1869.
Mendeléev left space for new elements, and predicted three yet-to-be-
discovered elements: Ga (1875), Sc (1879) and Ge (1886).
Discoverer of the Periodic Table —
an early “Chemoinformatician”
History of QSAR CO1
CO2.1
Friday, June 11, 2021 Abhijit Debnath | BP807ET-CADD | Unit-2
1868, D. Mendeleev – The Periodic Table of Elements

Chemical
properties of
elements
gradually vary
along the two axis
History of QSAR CO1
CO2.1
Dmitry Mendeleév
(1834 – 1907)
Discoverer of the Periodic Table —
an early “Chemoinformatician”
1868, D. Mendeleev – The Periodic Table of Elements

History of QSAR CO1
CO2.1
1868, A. Crum-Brown and T.R. Fraser –
- formulated a suggestion that physiological activity of molecules depends on their
constitution:
- They studied a series of quaternized strychnine derivatives, some of which possess activity
similar to curare in paralyzing muscle.
Activity = F(structure)
1869, B.J. Richardson – narcotic effect of primary alcohols varies in proportion to their molecular weights.

History of QSAR CO1
CO2.1
1893, C. Richet has shown that toxicities of some simple organic compounds (ethers, alcohols, ketones) were
inversely related to their solubility in water.
1899, H. Meyer and 1901, E. Overton have found variation of the potencies of narcotic compounds with LogP.
1904, J. Traube found a linear relation between narcosis and surface tension.
1937, L.P. Hammett studied chemical reactivity of substituted benzenes:
Hammett equation,
Linear Free Energy Relationship (LFER)

History of QSAR CO1
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1939, J. Fergusson formulated a concept linking narcotic activity, logP and thermodynamics.
1952- 1956, R.W. Taft devised a procedure for separating polar, steric and resonance effects.
1964, C. Hansch and T. Fujita: the biologist’s Hammett equation.
1964, Free and Wilson, QSAR on fragments.
1970s – 1980s – development of 2D QSAR (descriptors, mathematical formalism).
1980s – 1990s, development of 3D QSAR (pharmacophores, CoMFA, docking).
1990s – present, virtual screening.

Summary
History of QSAR CO1
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QSAR and Drug Design
CO2.1

QSAR and Drug
Design
 Conventional Drug Design
 Type of CADD Approach
 QSAR in Ligand Based Drug Design
 Types of QSAR
 Challenges in QSAR Modeling
CO2.1

Conventional Drug Design CO1
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1) Getting Lead Molecule Limitation: Time consuming and less
chances of success
2) Optimization of Lead Molecule Limitation: SAR based
optimization, which don't guide properly to the Researcher

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Type of CADD Approach CO2.1

QSAR in Ligand Based Drug Design CO1
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 Same experimental evaluation protocol (errors vary with
method to method)
 Biological activity range > 4 log order ( 0.1 to 1000nM)
with uniform distribution.
 Same active site ( allosteric vs Competitive)
 Stereochemistry and tautomerism should be well
defined ( avoid considering BA data of DL or mixture of
two tautomers)

What is QSAR ? CO1
CO2.1
 QSAR is computerised statistical method which correlate the structure with biological activity in terms of
physico-chemical properties
 QSARs are the mathematical relationships linking chemical structures with biological activity using
physicochemical or any other derived property as an interface.
 The number of compounds required for synthesis in order to place 10 different groups in 4
positions of benzene ring is 10“.
 Solution : synthesize a small number of compounds and from their bioactivity data derive rules
to predict the biological activity of other compounds.

 Mathematical Methods used in QSAR includes
various regression and pattern recognition
techniques.
 Physicochemical or any other property used for
generating QSARs is termed as Descriptors and
treated as independent variable.
 Biological property is treated as dependent
variable.
What is QSAR ? CO1
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What is QSAR ? CO1
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 It is a ligand based drug design method developed more than 50 years
ago by Hansch and Fujita.
 It positively aims in establishing the possibility of a relationship
between a set of descriptors and pharmacological response
It finds out the correlation between the chemical structure and the
biological activity (pIC50, PEC50. Ki, etc.) or categorical/binary (active,
inactive, toxic, nontoxic, etc.)

What is QSAR ? CO1
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What is QSAR ? CO1
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 Biological Activities
• Bioactivity
- Inhibition (IC50r Ki)
- Activation
- Modulation
•ADMET / efficacy
- Absorbance/efficacy
- Drug metabolism/interaction with CYP
- Excretion/ Pharmaco kinetics & dynamics
• Toxicity
- Selectivity to targets & associates

Applications of QSAR CO1
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1. Selection of Proper Substituents
2. Prediction of Biological Activity

Applications of QSAR CO1
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3. Identify & quantify the physicochemical properties of a drug
4. To see whether these properties has an effect on the drug's biological activity.
5. By quantifying the physicochemical properties, it is possible to calculate in advance the biological activity of a
novel drug.
6. From this, we can put efforts on analogues which should have improved activity & cut down the number of
analogues that has to be made.
7. If an analogue is discovered, which does not fit the equation, it implies some
other feature is important and provides a lead for further development.

Steps in QSAR CO1
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STEP 1: Selection of biologically active series (Structure + Biological activity)
STEP 2: Calculation of Various Physico Chemical Descriptors
STEP 3: Correlation of physico-chemical properties with Biological activity by QSAR methods
STEP 4: Getting equation
STEP 5: Designing of the compounds based upon QSAR equation
STEP 6: Predicting the biological activity of the designed compounds
STEP 7: Synthesis of the compounds

Steps in QSAR : CO1
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STEP 1: Selection of biologically active series (Structure + Biological activity)

Steps in QSAR : CO1
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STEP 2: Calculation of Various Physico Chemical Descriptors
-Roberto Todeschini and Viviana
Consonni
The Molecular Descriptor is the
final result of a logic and mathematical
procedure which transforms chemical
information encoded within a symbolic
representation of a molecule into a useful
number or the result of some standardized
experiment.

Steps in QSAR CO1
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Types of
Molecular
Descriptors

Molecular
Descriptors
Steps in QSAR CO1
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Steps in QSAR CO1
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Steps in QSAR CO1
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 Multiple Liner Regression (MLR)

Steps in QSAR CO1
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 Principle Component Analysis

Steps in QSAR CO1
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 Partial Least Square (PLS)

DRAGON
The software DRAGON calculates 1664 molecular descriptors divided in 20 blocks
Steps in QSAR CO1
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CODESSA Pro
 calculate a large variety of molecular descriptors on the basis of the 3D geometrical structure and/or quantum-
chemical parameters;
 develop (multi)linear and non-linear QSPR
Steps in QSAR CO1
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ISIDA program
 calculates fragment descriptors;
 develop (multi)linear and non-linear QSPR models
Steps in QSAR CO1
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Steps in QSAR CO1
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 Hansch Equation
Hansch equation usually relate the biological activity to the most commonly
used physico-chemical properties such as log P (partition coefficient), o
(Hammet Constant) and E (Taft's Steric factor) with biological activity.

Steps in QSAR CO1
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 Free-Wilson model
Here substituent constant based upon biological activities were used rather that physical properties

Steps in QSAR CO1
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 Kubiny’s Approch

Steps in QSAR CO1
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 Cluster Significance Analysis
Graph is plotted of biological activity on Y-axis and Physio-chemical parameters on X-Axis

Steps in QSAR CO1
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 Topliss Scheme
To optimize lead compound

Steps in QSAR CO1
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 Craig Plot
Craig plot consist of sigma values on the Y-axis and Pie values on X-axis of different substituent

Steps in QSAR CO1
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STEP 4: Getting equation

Steps in QSAR CO1
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STEP 5: Designing of the compounds based upon QSAR equation

Steps in QSAR CO1
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STEP 6: Predicting the biological activity of the designed compounds

Steps in QSAR CO1
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STEP 7: Synthesis of the compounds

Steps in QSAR CO1
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Check enzyme inhibition of the optimized compounds

Types of QSARCO1
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1D-QSAR
2D-QSAR
3D-QSAR
4D-QSAR
5D-QSAR
6D-QSAR

Types of QSAR 1D QSAR
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3D-QSAR : macroscopic target properties with computed atom-based descriptors derived from the spatial 3D
representation of the molecular structures

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Prerequisite for 3D QSAR
 Congeneric Series or Same scaffolds
 atleast > 2 R group substituents
 Activity diversity should be more than 3 log scale
 Uniform mechanism of action or binding mode
 Same assay condition or lab
 Preferred values are normalized values such as Pki,PIC50 etc
 Minimum number of molecules 25 molecules

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 Dependent Variables Preprocessing

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 Length of Data Points
25 to 100 Molecules
> 100 Molecules
> 100 Molecules
logistic regression, SVM, random
forest, decision trees, artificial
neural networks, Bayesian models
RNN/CNN
MLA, PCA, PLS

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3D Atom based QSAR Force-based QSAR

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In 3D Atom based QSAR, molecule atoms are treated as
spheres, and categorized as ONE of the following:
✓ H-bond donor
✓ Hydrophobic/non-polar
✓ Negative lonic
✓ Positive lonic
✓ Electron withdrawing/H-bond acceptor
✓ Misc
3D Atom based QSAR

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Two slightly different methods are implemented for fQSAR:
1. Force-field (i.e. COMFA): steric and electrostatic fields are
evaluated aggrid points surrounding the ligand.
2. Gaussian (i.e. COMSIA): property-based fields weighted as a Gaussian
function of the distance between the grid point and
the atom.
Fields include steric, electrostatic, hydrophobic, h-bond
acceptor, and h-bond donor.
In both instances, grid points are used as independent
variables for PLS model fitting
Force-based QSAR

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Comparative molecular field analysis (CoMFA)
 COMFA involves placing of molecules in a grid and to correlate field properties of the molecules with biological
activities
 Dick Crammer in 1988
 Steps
 Group of compounds having a common pharmacophore is selected.
 The 3-dimensional structures of reasonable conformation must be generated from 2-dimensional
structures.
 The energy minimized structures are fitted to each other using pharmacophore hypothesis.
 Molecules are then aligned using active analog approach, distance geometry method
 Once molecules are aligned, a grid or lattice is established which surrounds the sets of analogues in
potential receptor space.

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 Pharmacophore Selection  Identification of Pharmacophore

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 CoMFA Alignment

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General Workflow for running 3D QSAR

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4D-QSAR additionally including ensemble of ligand
configurations in 3D-QSAR

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5D-QSAR explicitly representing different
induced-fit models in 4D-QSAR

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6D-QSAR further incorporating solvation
models in 5D-QSAR

Challenges in QSAR Modeling CO1
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Very recent publication by John Dearden has cited and described 21 major errors associated with the QSAR
predictive model and how to recover.

Youtube /other Video Links
https://www.youtube.com/watch?v=SEXT6Pulxrc
https://www.youtube.com/watch?v=3Tvdf2AUekg
https://www.youtube.com/watch?v=tCEQesj50gg
Faculty Video Links/ Youtube & NPTEL Video Links and
Online Courses Details (if any)

SUMMARY
The Biological activity of a molecule depends on its physiochemical properties, which is derived form it
structural arrangement. Hanch’s equation is the base of modern days Machine Learning based
Screening system. Discovery of drugs nowadays has become so popular by using QSAR due to
advancement of QSAR. it is very easy to identify the relationship between the structure of a drug and is
biological activity.

DAILY QUIZ
Q.1. Drugs was not isolated from a natural source is…
a) isoniazid
b) artemisinin
c) quinine
d) morphine
Q. 2 . Strategies that increase the polarity and water solubility of a drug is…
a) Replacing an aromatic ring
b) Replacing an alkyl group
c) Removing polar functional groups
d) Adding extra alkyl groups

DAILY QUIZ
Q.3. What value does the regression coefficient have for a perfect fit?
a) 0.1
b) 1
c) 10
d) 100
Q.4. What does the symbol P represent in a QSAR equation?
a) pH
b) plasma concentration
c) partition coefficient
d) Prodrug

DAILY QUIZ
Q.5 Calculate the logP value for the structure shown; logP for
benzene = 2.13; π(OH) -0.67; π(CH3) 0.52.
a) 3.32
b) 0.94
c) 1.98
d) 2.13

DAILY QUIZ
Q.6 What does MR represent in a QSAR equation?
a) Molar refractivity is a steric factor
b) Molar refractivity is an electronic factor
c) Molar refractivity is a hydrophobic factor
d) Molar refractivity is a stereoelectronic factor
Q.7 What software programme is used to determine the Verloop steric parameter?
a) Alchemy
b) Chem3D
c) Sterimol
d) ChemDraw

DAILY QUIZ
Q.8. What does a negative value of σ signify for a substituent?
a) It is electron donating
b) It is electron withdrawing
c) It is neutral
d) It is hydrophobic
Q.9 What is the symbol π in a QSAR equation?
a) The hydrophobicity of the molecule
b) The electronic effect of a substituent
c) The substituent hydrophobicity constant
d) A measure of the steric properties for a substituent

DAILY QUIZ
Q.10 The QSAR equation relating the insecticidal activity of a series of diethyl phenylphosphonates
versus σ is shown below.
What does r represent?
a) The regression or correlation coefficient
b) The number of compounds tested
c) The standard deviation
d) The F test

WEEKLY ASSIGNMENT
Q.1 What Structural Activity
Q.2 Describe restriction endonucleases
Q.3 Write about the Types of physicochemical Parameters
Q.4 What is Partition coefficient
Q.5. What is Hammet’s substituent Constant
Q.6 What is Taft’s steric constant
Q.7 What is COMSIA.
Q.8 What is Hansch analysis.
Q.9 write about the Free Wilson analysis
Q.10 How you will employ QSAR in Drug Discovery

MCQ s
Q.1. QSAR Means
a) Qualatative structure–activity relationship
b) Quantitative structural–activity relationship
c) Quantitative structure–activition relationship
d) Quantitative structure–activity relationship
Q.2. ____known as the father of the concept of quantitative structure-activity relationship (QSAR)
a) Fuzi
b) Hansch
c) Albert Einstein
d) Basu Jagdish
Q.3 RF, SVM, and Naïve Bayesian are available in :
a) Orange
b) libSVM
c) Auto Dock
d) Elki

MCQ s
Q.4 DT, Naïve Bayes, and SVM are available in :
a) Knime
b) AZOrange
c) Mastro
d) Open Babel
Q.5 What software programme is used to determine the Verloop steric parameter?
a) Alchemy
b) Chem3D
c) Sterimol
d) ChemDraw
Q.6. QSPR Means____________________
a) Qaulatative structure property relationships
b) Quantitative structural property relationships
c) Quantitative structure property relationships
d) Quantitative structure property relation

MCQ s
Q.7. For validation of QSAR models, usually various strategies are adopted:
a) internal validation or cross-validation , External
b) blind external validation
c) Data randomization or Y-scrambling
d) All of the above
Q.8. A typical data mining based prediction uses __________________ for inducing a predictive learning model.
a) support vector machines,
b) decision trees,
c) artificial neural networks
d) All of the above

MCQ s
Q.9. Full form of CoMFA is
(a) Comparative molecular field analysis
(b) Comparative molecular Force analysis
(c) Competitive molecular field analysis
(d) Competitive Molar field analysis
Q.10 Principal steps of QSAR/QSPR including
a) Selection of Data set and extraction of structural/empirical descriptors
b) variable selection,
c) model construction
d) validation evaluation.
e) All of the above

EXPECTED QUESTIONS FOR UNIVERSITY EXAM
.
Q.1 What Structural Activity
Q.2 Describe restriction endonucleases
Q.3 Write about the Types of physicochemical parameters
Q.4 What is Partition coefficient
Q.5. What is Hammet’s substituent constant
Q.6 What is Taft’s steric constant
Q.7 What is COMSIA.

PREVIOUS YEAR QUESTION PAPER
Thursday, May 13, 2021 Abhijit Debnath | BP807ET-CADD | Unit-1 98
Friday, June 11, 2021 Abhijit Debnath | BP807ET-CADD | Unit-2

REFERENCES AND BOOKS TO BE FOLLOWED
• Delgado JN, Remers WA eds “Wilson & Gisvold’s Text Book of Organic Medicinal & Pharmaceutical Chemistry”
Lippincott, New York.
• Foye WO “Principles of Medicinal chemistry ‘Lea & Febiger.
• Koro lkovas A, Burckhalter JH. “Essentials of Medicinal Chemistry” Wiley Interscience.
• Wolf ME, ed “The Basis of Medicinal Chemistry, Burger’s Medicinal Chemistry” John Wiley & Sons, New York.

Quantitative Structure Activity Relationship (QSAR)

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