QSAR detail description pointwise includes substituent Hydrophobicity constant (pi), Hammett Substituent Constant (sigma), Steric Factors , Free Energy of Binding, Basic Assumption in QSAR , Free-Wilson Analysis,Hansch Analysis, Molecular docking strategies, Stages of Docking, Principal Component Analysis (PCA), CoMFA,

1. Quantitative Structure
Activity Relationships
(QSAR)
Dr. Shilpa Sudhakar Harak
GES Sir Dr. M. S. Gosavi College of Pharmaceutical Education &
Research
Nasik

2. Outline
• Introduction
• Structures and activities
• Analysis techniques:
Free-Wilson, Hansch
• Regression techniques:
PCA, PLS
• Comparative Molecular Field Analysis

3. QSAR: The Setting
Quantitative structure-activity relationships are used when
• there is little or no receptor information
• but there are measured activities of (many) compounds

4. From Structure to Property
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
0
1
2
3
4
5
6
7
8
9
1 3 5 7 9 11 13 15
EC50

5. From Structure to Property
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
LD50

6. From Structure to Property
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH
O
H
H H
OH

7. QSAR: Which Relationship?
Quantitative structure-activity relationships correlate
chemical/biological activities with structural features or
atomic, group or molecular properties.
within a range of structurally similar compounds

8. Quantitative measurements for biological &
physicochemical properties
Most common properties studied
• Hydrophobicity of the molecule
• Hydrophilicity of substituents
• Electronic properties of substituents
• Steric properties of substituents

9. Partition Coefficient P = [Drug in octanol]
[Drug in water]
High P High hydrophobicity
Hydrophobicity of the Molecule

10. •Activity of drugs is often related to P
e.g. binding of drugs to serum albumin
(straight line - limited range of log P)
•Binding increases as log P increases
•Binding is greater for hydrophobic drugs
Log 1
C



= 0.75 logP+ 2.30
Log (1/C)
Log P
. .
.
.. .
...
0.78 3.82
Hydrophobicity of the Molecule

11. Example 2 General anaesthetic activity of ethers
(parabolic curve - larger range of log P values)
Optimum value of log P for anaesthetic activity = log Po
Log
1
C



= -0.22(logP)2 + 1.04 logP + 2.16
Log P o
Log P
Log (1/C)
Hydrophobicity of the Molecule

12. QSAR equations are only applicable to compounds in the same
structural class (e.g. ethers)
• However, log Po is similar for anaesthetics of different
structural classes (ca. 2.3)
• Structures with log P ca. 2.3 enter the CNS easily
(e.g. potent barbiturates have a log P of approximately 2.0)
• Can alter log P value of drugs away from 2.0 to avoid CNS side
effects
Hydrophobicity of the Molecule

13. Example:
Benzene
(Log P = 2.13)
Chlorobenzene
(Log P = 2.84)
Benzamide
(Log P = 0.64)
Cl CONH2
pCl = 0.71 pCONH = -1.492
Hydrophobicity of Substituents (p)
- the substituent hydrophobicity constant
• A measure of a substituent’s hydrophobicity relative to hydrogen
• Tabulated values exist for aliphatic and aromatic substituents
• Measured experimentally by comparison of log P values with log P of
parent structure
• Positive values imply substituents are more hydrophobic than H
• Negative values imply substituents are less hydrophobic than H

14. Example:
meta-Chlorobenzamide
Cl
CONH2
Log P(theory) = log P(benzene) + pCl + pCONH
= 2.13 + 0.71 - 1.49
= 1.35
Log P (observed) = 1.51
2
Hydrophobicity of Substituents
- the substituent Hydrophobicity constant (p)
• The value of p is only valid for parent structures
• It is possible to calculate log P using p values
• A QSAR equation may include both P and p.
• 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

15. X=H K H = Dissociation constant= [PhCO 2-]
[PhCO 2H]
+CO2H CO2 H
X X
Electronic Effects
Hammett Substituent Constant (s)
• The constant (s) is a measure of the e-withdrawing or e-donating
influence of substituents
• It can be measured experimentally and tabulated (e.g. s for
aromatic substituents is measured by comparing the dissociation
constants of substituted benzoic acids with benzoic acid)

16. +
X = electron
withdrawing
group
X
CO2CO2H
X
H
s X = log
K X
K H
= logK X - logK H
Positive value
Hammett Substituent Constant (s)
• X= electron withdrawing group (e.g. NO2)
• Charge is stabilised by X
• Equilibrium shifts to right
• KX > KH

17. s X = log
K X
K H
= logK X - logK H
Charge destabilised Equilibrium shifts to left
KX < KH
Negative value
+
X = electron
withdrawing
group
X
CO2CO2H
X
H
Hammett Substituent Constant (s)
• X= electron donating group (e.g. CH3)

18. • s value depends on inductive and resonance effects
• s value depends on whether the substituent is meta or para
• ortho values are invalid due to steric factors
Hammett Substituent Constant (s)

19. DRUG
N
O
O
meta-Substitution
EXAMPLES: sp (NO2) =0.78 sm (NO2) =0.71
e-withdrawing (inductive effect only)
e-withdrawing (inductive + resonance effects)
N
O O
DRUG DRUG
N
OO
N
O O
DRUG DRUG
N
OO
para-Substitution
Hammett Substituent Constant (s)

20. sm (OH) =0.12 sp (OH) =-0.37
e-withdrawing (inductive effect only)
e-donating by resonance more important than inductive effect
EXAMPLES:
DRUG
OH
meta-Substitution
DRUG
OH
DRUG DRUG
OH OH
DRUG
OH
para-Substitution
Hammett Substituent Constant (s)

21. QSAR Equation:
Diethylphenylphosphates (Insecticides)
log 1
C



= 2.282s - 0.348
Conclusion: e-withdrawing substituents increase activity
X
O P
O
OEt
OEt
Hammett Substituent Constant (s)

22. Electronic Factors R & F
• R - Quantifies a substituent’s resonance effects
• F - Quantifies a substituent’s inductive effects

23. X= electron donating Rate sI = -ve
X= electron withdrawing Rate sI = +ve
+
Hydrolysis
HOMe
CH2 OMe
C
O
X CH2 OH
C
O
X
Aliphatic electronic substituents
• Defined by sI
• Purely inductive effects
• Obtained experimentally by measuring the rates of hydrolyses of
aliphatic esters
• Hydrolysis rates measured under basic and acidic conditions
• Basic conditions: Rate affected by steric + electronic factors
Gives sI after correction for steric effect
• Acidic conditions: Rate affected by steric factors only (see Es)

24. Steric Factors
Taft’s Steric Factor (Es)
• Measured by comparing the rates of hydrolysis of substituted
aliphatic esters against a standard ester under acidic conditions
Es = log kx - log ko
kx represents the rate of hydrolysis of a substituted ester
ko represents the rate of hydrolysis of the parent ester
• Limited to substituents which interact sterically with the tetrahedral
transition state for the reaction
• Cannot be used for substituents which interact with the transition
state by resonance or hydrogen bonding
• May undervalue the steric effect of groups in an intermolecular
process (i.e. a drug binding to a receptor)

25. Steric Factors
Molar Refractivity (MR) - a measure of a substituent’s volume
MR =
(n 2
-1)
(n 2
- 2)
x
mol. wt.
density
Correction factor
for polarisation
(n=index of
refraction)
Defines volume
Steric Factors
Molar Refractivity (MR)

26. Verloop Steric Parameter
- calculated by software (STERIMOL)
- gives dimensions of a substituent
- can be used for any substituent
L
B 3
B 4
B4 B3
B2
B1
C
O
O
H
H O C O
Example - Carboxylic acid
Steric Factors

27. Free Energy of Binding and
Equilibrium Constants
The free energy of binding is related to the reaction
constants of ligand-receptor complex formation:
DGbinding = –2.303 RT log K
= –2.303 RT log (kon / koff)
Equilibrium constant K
Rate constants kon (association) and koff (dissociation)

28. Concentration as Activity Measure
• A critical molar concentration C that produces the biological
effect is related to the equilibrium constant K
• Usually log (1/C) is used (c.f. pH)
• For meaningful QSARs, activities need to be spread out over
at least 3 log units

29. Free Energy of Binding
DGbinding = DG0 + DGhb + DGionic + DGlipo + DGrot
DG0 entropy loss (translat. + rotat.) +5.4
DGhb ideal hydrogen bond –4.7
DGionic ideal ionic interaction –8.3
DGlipo lipophilic contact –0.17
DGrot entropy loss (rotat. bonds) +1.4
(Energies in kJ/mol per unit feature)

30. Basic Assumption in QSAR
The structural properties of a compound contribute
in a linearly additive way to its biological activity provided there
are no non-linear dependencies of transport or binding on some
properties

31. An Example: Capsaicin Analogs
X EC50(mM) log(1/EC50)
H 11.80 4.93
Cl 1.24 5.91
NO2 4.58 5.34
CN 26.50 4.58
C6H5 0.24 6.62
NMe2 4.39 5.36
I 0.35 6.46
NHCHO ? ?
X
N
H
O
OH
MeO

32. An Example: Capsaicin Analogs
X log(1/EC50) MR p s Es
H 4.93 1.03 0.00 0.00 0.00
Cl 5.91 6.03 0.71 0.23 -0.97
NO2 5.34 7.36 -0.28 0.78 -2.52
CN 4.58 6.33 -0.57 0.66 -0.51
C6H5 6.62 25.36 1.96 -0.01 -3.82
NMe2 5.36 15.55 0.18 -0.83 -2.90
I 6.46 13.94 1.12 0.18 -1.40
NHCHO ? 10.31 -0.98 0.00 -0.98
MR = molar refractivity (polarizability) parameter; p = hydrophobicity parameter;
s= electronic sigma constant (para position); Es = Taft size parameter

33. An Example: Capsaicin Analogs
X
N
H
O
OH
MeO
log(1/EC50) = -0.89 + 0.019 * MR +
0.23 * p + (-10.31) * s + (-0.14) * Es

34. An Example: Capsaicin Analogs
X EC50(mM) log(1/EC50)
H 11.80 4.93
Cl 1.24 5.91
NO2 4.58 5.34
CN 26.50 4.58
C6H5 0.24 6.62
NMe2 4.39 5.36
I 0.35 6.46
NHCHO ? ?
X
N
H
O
OH
MeO

35. First Approaches: The Early Days
• Free- Wilson Analysis
• Hansch Analysis

36. Free-Wilson Analysis
• The biological activity of the parent structure is measured & compared with
the activity of analogues bearing different substituents
• An equation is derived relating biological activity to the presence or absence
of particular substituents
• Activity = k1X1 + k2X2 +.…knXn + Z
• Xn is an indicator variable which is given the value 0 or 1 depending on
whether the substituent (n) is present or not
• The contribution of each substituent (n) to activity is determined by the value
of kn
• Z is a constant representing the overall activity of the structures studied

37. Free-Wilson Analysis
log (1/C) = S aixi + m
xi: presence of group i (0 or 1)
ai: activity group contribution of group i
m: activity value of unsubstituted compound

38. Free-Wilson Analysis
+ Computationally straightforward
– Predictions only for substituents already included
– Requires large number of compounds

39. Advantages
• No need for physicochemical constants or tables
• Useful for structures with unusual substituents
• Useful for quantifying the biological effects of molecular features that
cannot be quantified or tabulated by the Hansch method
Disadvantages
• 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
• The effects of different substituents may not be additive
• (e.g. intramolecular interactions)
Free-Wilson Analysis

40. Hansch Analysis
Drug transport and binding affinity depend nonlinearly on lipophilicity:
log (1/C) = a (log P)2 + b log P + c Ss + k
P: n-octanol/water partition coefficient
s: Hammett electronic parameter
a, b, c: regression coefficients
k: constant term

41. Hansch Analysis
+ Fewer regression coefficients needed for correlation
+ Interpretation in physicochemical terms
+ Predictions for other substituent's possible

42. Molecular Descriptors
• Simple counts of features, e.g. of atoms, rings, H-bond donors,
molecular weight
• Physicochemical properties, e.g. polarisability, hydrophobicity
(logP), water-solubility
• Group properties, e.g. Hammett and Taft constants, volume
• 2D Fingerprints based on fragments
• 3D Screens based on fragments

43. 2D Fingerprints
Br
N
H
O
OH
MeO
C N O P S X F Cl Br I Ph CO NH OH Me Et Py CHO SO C=C CΞC C=N Am Im
1 1 1 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0

44. • Molecular docking strategies & different
methods of
• docking. Mechanism based drug design
including quantum mechanics,
• molecular mechanics and molecular modeling

45. MOLECULAR DOCKING STRATEGIES

46. Molecular Docking
What? How? Why?
• In silico (computer-based) approach
• Identification of bound conformation
• Prediction of binding affinity
• Docking vs. (Virtual) Screening

47. Two “Modes”: – Respective:
• How does your molecule bind?
• What is its mode of action?
• What might be the reaction mechanism?
Molecular Docking
What? How? Why?

48. – Prospective:
• What compounds might be good leads?
• What compound(s) should you make?
Molecular Docking
What? How? Why?

49. Docking Basics
• Initially – Receptor (protein) and ligand
rigid
• Most current approaches – Receptor rigid,
ligand flexible
• Advanced approaches – Receptor (to a
degree) and ligand flexible Fast, Simple
Slow, Complex
FAST/ SIMPLE
SLOW /COMPLEX

50. Docking

51. Stages of Docking
• Pose generation
– Place the ligand in the binding site
– Generally well solved
• Pose selection
– Determine the proper pose
– The hard part

52. Pose Generation
• Rigid docking with a series of conformers
– Most techniques use this approach
– Most techniques will generate the conformers internally rather
than using conformers as inputs
• Incremental construction (FlexX)
• – Split ligand into base fragment and side-chains – Place base
– Add side-chains to grow, scoring as you grow
• In general, use a very basic vdW shape function
• Often see variability with input conformers

53. POSE SELECTIONPose Selection/Scoring

54. Pose Selection/Scoring
• Where most of the current research focused
• More sophisticated scoring functions take longer
– Balance need for speed vs. need for accuracy
– Virtual screening needs to be very fast
– Studies on single compounds can be much slower
– Can do multi-stage studies

55. Regression Techniques
• Principal Component Analysis (PCA)
• Partial Least Squares (PLS)

56. Principal Component Analysis (PCA)
• Many (>3) variables to describe objects = high dimensionality of
descriptor data
• PCA is used to reduce dimensionality
• PCA extracts the most important factors
(principal components or PCs) from the data
• Useful when correlations exist between descriptors
• The result is a new, small set of variables (PCs) which explain most of
the data variation

57. PCA – From 2D to 1D

58. PCA – From 3D to 3D-

59. Different Views on PCA
• Statistically, PCA is a multivariate analysis technique closely
related to eigenvector analysis
• In matrix terms, PCA is a decomposition of matrix X
into two smaller matrices plus a set of residuals:
X = TPT + R
• Geometrically, PCA is a projection technique in which X is
projected onto a subspace of reduced dimensions

60. Partial Least Squares (PLS)
y1 = a0 + a1x11 + a2x12 + a3x13 + … + e1
y2 = a0 + a1x21 + a2x22 + a3x23 + … + e2
y3 = a0 + a1x31 + a2x32 + a3x33 + … + e3
…
yn = a0 + a1xn1 + a2xn2 + a3xn3 + … + en
Y = XA + E
(compound 1)
(compound 2)
(compound 3)
…
(compound n)
X = independent variables
Y = dependent variables

61. PLS – Cross-validation
• Squared correlation coefficient R2
• Value between 0 and 1 (> 0.9)
• Indicating explanative power of regression equation
• Squared correlation coefficient Q2
• Value between 0 and 1 (> 0.5)
• Indicating predictive power of regression equation
With cross-validation:

62. PCA vs PLS
• PCA:
The Principle Components describe the variance
in the independent variables (descriptors)
• PLS:
The Principle Components describe the variance
in both the independent variables (descriptors)
and the dependent variable (activity)

63. Comparative Molecular Field Analysis
(CoMFA)
• Set of chemically related compounds
• Common substructure required
• 3D structures needed (e.g., Corina-generated)
• Bioactive conformations of the active compounds
are to be aligned

64. CoMFA Alignment
C7
OH
OH
A
D
B
C1
MeO OMe
ClCl
Cl
BA
O
O
C7
OH
OH
OH
A
B
C1
O
NMe2
OH
A B
CL
L
L d1
d2
d3L
L
L
d1
d2
d3
L
L
L
d1
d2
d3
L
L
L
d1 d2
d3
L
L
L
d1
d2
d3
"Pharmacophore"

65. CoMFA Grid and Field Probe
(Only one molecule shown for clarity)

66. Electrostatic Potential Contour Lines

67. CoMFA Model Derivation
Van der Waals field
(probe is neutral carbon)
Evdw = S (Airij
-12 - Birij
-6)
Electrostatic field
(probe is charged atom)
Ec = S qiqj / Drij
• Molecules are positioned in a regular grid
according to alignment
• Probes are used to determine the molecular field:

68. 3D Contour Map for Electronegativity

69. CoMFA Pros and Cons
+ 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