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8/3/2019 Urmila Joshi

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2D QSAR


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QSAR IN DRUG DESIGN

To introduce order in the universe, man must

pay attention to the quantitative aspects of

the universe and try to find a mathematical

relationship between them.

--------- Galileo Galili


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HistoryofQSAR

Biological properties of molecules are related totheir structure.

A mathematical relationship between thestructure and biological properties was proposedat the end of the 19th century

Hydrophobicity was the first physicochemicalproperty which showed a quantitativerelationship with narcotic activity


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HistoryofQSAR

Crum-Brown and Fraser Postulate : = f (C)

Meyer and Overtone : Practical Evidence :

Toxicity as a function of Lipophilicity

Fergussons Principle : Depressant action

related to the relative saturation in vapourphase. First attempt to use a thermodynamic

constant


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HistoryofQSAR

Use of Substituent Constants :

1. Hammett Constant

2. Taft Steric Constant Es and the Hancock

correction

3. Lipophilicity Constant


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A VarietyofPhysicochemical

Parameters Lipophilicity : log P, , Rm

Electronic Effect : , F, R, Dipole moments,Spectral shifts, Ionization constants, Quantum

chemical indices for electron density

Steric Parameters :


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MolecularConnectivity Index


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MolecularDescriptors

Calculated/Computed : Can be calculated

using a mathematical procedure that converts

chemical structure/ information into a number

Experimentally determined : using

standardized experiments to measure some

molecular attributes


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Whydo weneed Descriptors?

Describe different aspects of molecules

Compare different molecular structures

Compare different conformation of same

molecule

Database storage


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MolecularDescriptors

Structural information is given by molecular

descriptors

Molecular descriptors can be classified as

1D, 2D and 3D descriptors

1D descriptors give information about thewhole molecule, 2D about the substituent and

3D about the molecular fields


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TypesofDescriptors

Counts of features: For example HBAs, HBDs,

aromatic ring systems, substructures/fragments (

e.g. , carbonyl groups, basic nitrogens, carboxyl

groups,),etc.

Physicochemical Properties: LogP, solubility,

MW, MP, BP, heat of sublimation, molarrefractivity, Hammett parameters, etc.


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TypesofDescriptors Contd.

Topological Indices: Wiener index, branching

indices, kappa shape indices, electrotopological state

indices, atom-pairs, topological torsions, etc.

BCUTs (3-D, 2-D, 2-T): Electrostatic, charge, and

polarizability (hydrophobic).

Others: Volsurf, polar surface area, etc.


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HistoryofQSAR

Subsequent efforts resulted in electronic and the

steric parameters being correlated with biological

activity.

The first successful application of QSAR is credited

to the efforts of Prof. Corwin Hansch, who developed

an equation correlating biological activity of a set of

molecules to a linear combination of lipophilicy and

electronic effects of a series of closely relatedmolecules


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ModelsinMedicinal Chemistry

Model is a transformation of a pototype which

can be more conveniently handled.

Need for a Model : Ethical considerations and

Reduction in complexity

Types of Models


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What is QSAR

A QSAR is a mathematical relationship between a

biological activity of a molecular system and its

geometric and chemical characteristics.

QSAR attempts to find consistent relationship

between biological activity and molecular

properties, so that these rules can be used to

evaluate the activity of new compounds.


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Why QSAR?

The number of compounds required for

synthesis in order to place 10 different groups

in 4 positions of benzene ring is 104

Solution: synthesize a small number of

compounds and from their data derive rules

to predict the biological activity of other

compounds.


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NecessitiesofQSAR

Good input data

Meaningful Structural Information

Predictive Models


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Experimental Data Set

Needs of Experimental Data :

1. As numerous as possible

2. Correct

3. Representative

4. Homogenous ( ideally same lab, same

method)


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Criteria ofExperimental Dataset

Compounds should belong to the same

congeneric series

The compounds should have similar binding

mode

Binding affinity should correlate with

interaction energy

Biological activity should correlate withbinding affinity


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Garbagein- Garbageout

The models will only be as good as

the dataset used to develop them


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A Dataset will looklikethis!

No. Comp. Activity

1 A-1 1.23

2 A-2 1.87

3 A-3 2.65

4 A-4 2.08

5 A-5 1.956 A-6 2.43

7 A-7 2.28


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A Dataset Along With DescriptorsA Dataset Along With Descriptors

XX log(1/EClog(1/EC5050))MRMR TT WW EEss

HH 4.934.93 1.031.03 0.000.00 0.000.00 0.000.00

ClCl 5.915.91 6.036.03 0.710.71 0.230.23 --0.970.97

NONO22 5.345.34 7.367.36 --0.280.28 0.780.78 --2.522.52

CNCN 4.584.58 6.336.33 --0.570.57 0.660.66 --0.510.51

CC66HH55 6.626.62 25.3625.36 1.961.96 --0.010.01 --3.823.82

NMe2NMe2 5.365.36 15.5515.55 0.180.18 --0.830.83 --2.902.90

II 6.466.46 13.9413.94 1.121.12 0.180.18 --1.401.40

NHCHO ?NHCHO ? 10.3110.31 --0.980.98 0.000.00 --0.980.98


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TooMany Descriptors!!

Reduce to manageable size by

1. Principal Component Analysis

2. Cluster Analysis

3. Choice of important Descriptors : Remove

Descriptors which show similar value for 90%

of the compounds

4. Investigators Intervention : Computers do

mathematics, they do not understand biology


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NumberofDescriptors

The data set should contain at least 5 times as many

compounds as the no. of descriptor in the QSAR if

MLR is used as a method; and 60% of the no. of

compounds if PLS is used as a method

Too few compounds relative to the number of

descriptors will give a false meaningless and high

correlation


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Biological Data ForQSAR

Experimentally generated data

Reported Data


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HanschModel

Steps Involved

1. Selection of Lead Compound2. Selection of Substituents

3. Synthesis and Biological Evaluation

4. Determination of Descriptors5. Generation of Regression Equation


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SelectionofSubstituents

Batchwise Selection

Stepwise Selection


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IndicatorVariable

Indicated by I

Arbitrorily assigned only two values, 1 and 0

Should always be used along with other

Descriptors, and that descriptor should be

significant in the regression equation even inabsence of an indicator variable.


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Mathematical Relationship

QSAR attempts to find a mathematicalrelationship between Descriptors and thebiological activity in form of an equation

This is traditionally done using Regressionanalysis

Descriptors are considered to be independentparameters and the biological activity as thedependent parameter


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Statistical Techniques

Two major statistical techniques are used for

this purpose

1. MLR : Multiple Linear Regression

2. PLS : Partial Least Squares


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NewerTechniques

Artificial Neural Networks

Genetic Algorithm

Advantages : Can detect nonlinear

relationship between the descriptors and

biological activity


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TheMathematical Result

The result is an equation with associated

statistical parameters

The Statistical Parameters are :1. n

2. r

3. s4. F


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Putting it all together

For a group of antihistamines,

Log (1/C) = 0.440 Es 2.204

(n=30, s=0.307, r= 0.886)Log (1/C) = 2.814 W - 0.223

(n=30, s=0.519, r= 0.629)

Log (1/C) = 0.492 Es - 0.585 W- 2.445

(n=30, s= .301, r= 0.889)


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ValidationofQSAR Equations

Statistical Parameters and their significance

Scrambling the Y-values (Biological activity)

Leave One Out and Leave Many Out Method

Test set-Training set Method


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InterpretationofEquations and Use

Prediction of Activity ofUnknown Compounds

Improving the series of compounds with

reference to the biological activity bysynthesis of new and active compounds

Restricting the number of compounds

synthesized for maximising the activity


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LimitationsofQSAR

False correlations due to noisy data

False positives is a major problem as compared tofalse negatives

Statistical Gimmick

Metabolism of the compounds not taken intoconsideration

Alternate binding modes may affect the results in asignificant way.


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3D QSAR : CoMFA

Cramer and Milne (1979)

Comparison of molecules by alignment and

field generation

Wold (1986)

Proposes using PLS instead of PCA for

overrepresented (1000s of field non-orthogonal

variables) problem Cramer, Patterson andBunce (1988)

Introduced CoMFA


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Free Energyof Binding andEquilibriumConstants

The free energy of binding is related to thereaction constants of ligand-receptor complexformation:

Gbinding = 2.303 RT log K= 2.303 RT log (kon / koff)

Equilibrium constant K

Rate constants kon (association) and koff(dissociation)


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Free Energyof Binding

(Gbinding = (G0+ (Ghb+ (Gionic + (Glipo + (Grot

(G0 entropy loss (translat. + rotat.) +5.4

(Ghb

ideal hydrogen bond 4.7

(Gionic ideal ionic interaction 8.3

(Glipo lipophilic contact 0.17

(Grot entropy loss (rotat.bonds) +1.4

(Energies in kJ/mol per unit feature)


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CoMFA

Set of chemically related compounds

3D structures needed

Bioactive conformations of the active

compounds are to be aligned


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CoMFA Alignment

L

LL

d1

d2

d3

L

LL

d1

d2

d3

"Pharmacophore"

C7OH

OH

A

D

B

L

LL

d1

d2

d3

O

OC

7OH

OHOH

A

B


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CoMFA Grid and Field Probe


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Molecular Fields in CoMFA

CoMFA standard: steric and electrostatic,

additional: H-bonding, indicator, parabolic and

others.

A grid with energyfieldsiscalculatedbyplacing a probe atom ateach voxel.

Themolecularfields are:

Steric (Lennard-Jones)interactionsElectrostatic (Coulombic)interactions

A probeissp3 carbon atom with chargeof+1.0


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Common 3D molecular fields

MEP Molecular Electrostatic Potential (unit

positive charge probe).

MLP Molecular Lipophilicity Potential (no

probe necessary).

GRID total energy of interaction: the sum of

steric (Lennard-Jones), H-bonding and

electrostatics (any probe can be used).


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Themolecularfields are:

Steric (Lennard-Jones)interactions

Electrostatic (Coulombic)interactions

A probeissp3 carbon atom with chargeof+1.0

El t t ti P t ti l C t


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Electrostatic PotentialContourLines


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3DContour Map forElectronegativity


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CoMFA Pros andCons

Suitable to describe receptor-ligand

interactions

3D visualization of important features Good correlation within related set

Predictive power within scanned space

Alignment is often difficult

Training required


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3D-QSAR: CoMFA

1st needtostructurally align

2-D Alignment Methods Maximum common substructure based methods

Feature-Based

Vector Methods

Discrete feature values (Bit Strings)

Continuous feature values

3-D Alignment Methods

Field-based methods

Structure-based methods (generalized RMSD approaches)

S b S


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Subsequent Steps

Calculate property fields for each molecule at every

grid point (training set)

Property value at each grid point is equivalent to a

descriptor value in 2-D QSAR

Grid points with low variance may be neglected;

nevertheless this may result in hundreds of grid points

Many more descriptor values than experimental data

points, thus traditional least-squares approach cannot

be used Perform partial least squares (PLS) analysis

Validate model (test set)

Predict activities of new molecules


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CoMFA CountourPlots

Inactive Molecule Active Molecule


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TheHansch equation

ORJ&.ORJ3 .ORJ3.

.

:KHUH...DQG.DUHFRQVWDQWV

/RJ3LVWKHSDUWLWLRQFRHIILFLHQW

is the substituent constant describing the

electronic effect of the substituent

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