SlideShare a Scribd company logo
1 of 25
BY: Satyendra Yadav
 Quantitative Structure Activity Relationship(QSAR)
is a set of methods that tries to find a mathematical
relationship between a set of descriptors of molecules
and their activity.
 The descriptors can be experimentally or
computationally derived. Using regression analysis, one
can extract a mathematical relationship between
chemical descriptors and activity.
 The QSAR approach attempts to identify and quantify
the physicochemical properties of a drug and to see
whether any of these properties has an effect on the
drug‘s biological activity.
 such a relationship holds true, an equation can be
drawn up which quantifies the relationship and allows
the medicinal chemist to say with some confidence
that the properties has an important role in the
distribution or mechanism of the drug.
 It also allows the medicinal chemist some level of
prediction. By quantifying physicochemical properties,
it should be possible to calculate in advance what the
biological activity of a novel analogue might be.
 The formulation of thousands of equations using
QSAR methodology attests to a validation of its
concepts and its utility in the elucidation of the
mechanism of action of drugs at the molecular
level and a more complete understanding of
physicochemical phenomena such as
hydrophobicity.
 Crum-Brown and Fraser expressed that the
physiological action of a substance was a function
of its chemical composition and constitution.
1. Allows the medicinal chemist to target efforts
on analogues which should have improved activity
and thus, decreases the number of analogues
which have to be made.
2. If an analogue is discovered which does not fit
the equation, it implies that some other feature
is important and provides a lead for further
development.
 They refer to any structural, physical, or chemical
property of a drug.
 The drug will have a large number of such properties
and it would be a very difficult task to quantify and
relate them all to biological activity at the same time.
 A simple more practical approach is to consider one or
two physicochemical properties of the drug and to
vary these while attempting to keep other properties
constant. This is not as simple as it sounds, since it is
not always possible to vary one property without
affecting another.
 The QSAR study then considers how the hydrophobic,
electronic, and steric properties of the substituents
affect biological activity.
 Hydrophobic character of a drug is crucial to how
easily it crosses cell membranes and may also be
important in receptor interactions. Changing
substituents on a drug may well have significant
effects on its hydrophobic character and hence its
biological activity.
 The partition coefficient (P)
p=Concentration of drug in
octanol/Concentration of drug in aqueous
solution
 The hydrophobic character of a drug can be measured
experimentally by testing the drug's relative
distribution in an octanol/water mixture. Hydrophobic
molecules will prefer to dissolve in the octanol layer of
this two-phase system, whereas hydrophilic molecules
will prefer the aqueous layer. The relative distribution
 Hydrophobic compounds will have a high P value,
whereas hydrophilic compounds will have a low P value.
 By plotting these P values against the biological
activity of these drugs, it is possible to see if there is
any relationship between the two properties.
 The graph is drawn by plotting log (1/C) versus log P.
 The scale of numbers involved in measuring C and
P usually covers several factors of ten and so the
use of logarithms allows the use of more
manageable numbers.
 Where the range of the logP values is restricted
to a small range (e.g. logP = 1-4), a straight-line
graph is obtained showing that there is a
relationship between hydrophobicity and biological
activity. Such a line would have the following
equation:
log (1/C) =k1 log p+k2
 For example, the binding of drugs to serum
albumin is determined by their hydrophobicity
and a study of 40 compounds resulted in the
following equation:
log (1/C) = 0.75 log P + 2.30
 The equation shows that serum albumin
binding increases as log P increases. In other”
words, hydrophobic drugs bind more strongly
to serum albumin than hydrophilic drugs.
 As an example of this, the cardiotonic agent
was found to produce 'bright visions' in some
patients, which implied that it was entering
the CNS.
 This was supported by the fact that the log P
value of the drug was 2.59. In order to prevent
the drug entering the CNS, the 4-OMe group was
replaced with a 4-S(O)Me group. This particular
group is approximately the same size as the
methoxy group, but more hydrophilic. The logP
value of the new drug (sulmazole) was found to be
1.17. The drug was now to hydrophilic to enter the
CNS and was free of CNS side-effects.
 The electronic effects of various substituents will
clearly have an effect on a drug‘s ionization or
polarity. This in turn may have an effect on how easily
a drug can pass through cell membranes or how
strongly it can bind to a receptor.
 As far as substituents on an aromatic ring are
concerned, the measure used is known as the Hammett
substitution constant .
 The Hammett substitution constant ( ) is a measure
of the electron withdrawing or electron donating
ability of a substituent and has been determined by
measuring the dissociation of a series of substituted
benzoic acids compared to the dissociation of benzoic
acid itself.
 Benzoic acid is a weak acid and only partially
ionizes in water. An equilibrium is set up between
the ionized and non-ionized forms.
 Dissociation constant (KH)=[PhCO0¯]/[PhCO0H]
Ionization of benzoic acid
 When a substituent is present on the aromatic
ring, this equilibrium is affected. Electron
withdrawing groups, such as a nitro group, result
in the aromatic ring having a stronger electron
withdrawing and stabilizing influence on the
carboxylate anion.
 The equilibrium will therefore shift more to the
ionized form such that the substituted benzoic
acid is a stronger acid and has a larger Kx value.
 If the substituent X is an electron donating group
such as an alkyl group, then the aromatic ring is less
able to stabilize the carboxylate ion. The equilibrium
shifts to the left and a weaker acid is obtained with a
smaller Kx value.
 The Hammett substituent constant ( ) for a
particular substituent (X) is defined by the following
equation:
x = log Kx/KH = log Kx - log KH
 Benzoic acids containing electron withdrawing
substituents will have larger KX values than benzoic
acid itself (KH) and therefore the value of x for an
electron withdrawing substituent will be positive.
Substituents such as Cl, CN, or CF3 have positive a
values.
 Benzoic acids containing electron donating
substituents will have smaller Kx values than
benzoic acid itself and hence the value of x for
an electron donating substituent will be negative.
 Substituents such as Me, Et, and Bu' have
negative a values. The Hammett substituent
constant for H will be zero.
 The Hammett constant takes into account both
resonance and inductive effects. Therefore, the
value of a for a particular substituent will depend
on whether the substituent is meta or para.
 These are absolutely necessary for a drug
molecule to engage into a viable interaction either
with a drug receptor or with an enzyme.
 Obviously, this essentially demands certain
specific criteria that a drug molecule must fulfil
as: bulk, size and shape of the drug. The bulky
substituent more or less serve as a shield that
eventually hinders the possible and feasible
interaction taking place between a drug and a
receptor.
 The quantitation of steric effects is complex at
best and challenging in all other situations,
particularly at the molecular level.
 An added level of confusion comes, when attempts
are made to delineate size and shape.
Nevertheless, sterics are of overwhelming
importance in ligand-receptor interactions as well
as in transport phenomena in cellular systems. The
first steric parameter to be quantified and used
in QSAR studies was Taft's(Es) constant. Es is
defined as,
 where k, and k, represent the rates of acid
hydrolysis of esters, XCH2COOR and CH3COOR,
respectively.
Es = log(Kx/KH)
 Molar refractivity (MR)is usually designated as a
simple measure of the volume occupied either by an
individual atom or a cluster (group) of atoms.
 It is one of the most widely used steric parameter.
where, n = Index of refraction,
MW = Molecular Weight,
d = Density,
MW/d = Volume and
n²–1/n²+2 =Correction factor (i.e., how easily the
substituent can undergo polarization)
 Molar refractivity is specifically significant in a
situation when the substituent possesses either π
electron or lone pairs of electrons.
 It incorporate a Polarizability component that may
describe cohesion and is related to London
dispersion forces.
 The failure of the MR descriptor to adequately
address three-dimensional shape issues led to
Verloop‘s development of sterimol parameters.
 The unique revelation and wisdom of a latest computer
researched programme termed as sterimol has indeed
helped a long way in measuring the steric factor to a
reasonably correct extent. It essentially aids in the
calculation of desired steric substituent values
(Verloop steric parameters) based on various standard
physical parameters, such as : Vander Waals radii,
bond lengths, bond angles, and ultimately the proposed
most likely conformations for the substituent under
examination.
 It is, however, pertinent to mention here that unlike
the Taft’s steric factor (ES) the Verloop steric
parameters may be measured conveniently and
accurately for any substituent.
Verloop Steric Parameters for a Carboxylic Acid (—
COOH) Moiety
Example : Carboxylic acid (Benzoic Acid) : The ensuing
Verloop steric parameters for a carboxylic acid moiety are
duly measured, where L represents the length of the
substituent, and B1 – B4 designate the radii (i.e., longitudinal
and horizontal) of the two functional groups viz., carboxyl
and hydroxyl (—O—H).
Ligand based drug design

More Related Content

What's hot

Structure based drug design
Structure based drug designStructure based drug design
Structure based drug designADAM S
 
Molecular docking
Molecular dockingMolecular docking
Molecular dockingRahul B S
 
Structure based in silico virtual screening
Structure based in silico virtual screeningStructure based in silico virtual screening
Structure based in silico virtual screeningJoon Jyoti Sahariah
 
In Silico methods for ADMET prediction of new molecules
 In Silico methods for ADMET prediction of new molecules In Silico methods for ADMET prediction of new molecules
In Silico methods for ADMET prediction of new moleculesMadhuraDatar
 
Pharmacophore modeling
Pharmacophore modelingPharmacophore modeling
Pharmacophore modelingDevika Rana
 
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENT
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENTPHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENT
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENTShikha Popali
 
Molecular docking
Molecular dockingMolecular docking
Molecular dockingpalliyath91
 
De novo drug design
De novo drug designDe novo drug design
De novo drug designmojdeh y
 
Qsar and drug design ppt
Qsar and drug design pptQsar and drug design ppt
Qsar and drug design pptAbhik Seal
 
Computer aided drug designing
Computer aided drug designingComputer aided drug designing
Computer aided drug designingMuhammed sadiq
 
Energy minimization methods - Molecular Modeling
Energy minimization methods - Molecular ModelingEnergy minimization methods - Molecular Modeling
Energy minimization methods - Molecular ModelingChandni Pathak
 
COMPUTER AIDED DRUG DESIGN
COMPUTER AIDED DRUG DESIGNCOMPUTER AIDED DRUG DESIGN
COMPUTER AIDED DRUG DESIGNRaniBhagat1
 
Pharmacophore Modeling in Drug Designing
Pharmacophore Modeling in Drug DesigningPharmacophore Modeling in Drug Designing
Pharmacophore Modeling in Drug DesigningVinod Tonde
 

What's hot (20)

Structure based drug design
Structure based drug designStructure based drug design
Structure based drug design
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
22.pharmacophore
22.pharmacophore22.pharmacophore
22.pharmacophore
 
Docking
DockingDocking
Docking
 
Structure based in silico virtual screening
Structure based in silico virtual screeningStructure based in silico virtual screening
Structure based in silico virtual screening
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
In Silico methods for ADMET prediction of new molecules
 In Silico methods for ADMET prediction of new molecules In Silico methods for ADMET prediction of new molecules
In Silico methods for ADMET prediction of new molecules
 
Pharmacophore modeling
Pharmacophore modelingPharmacophore modeling
Pharmacophore modeling
 
Pharmacophore
PharmacophorePharmacophore
Pharmacophore
 
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENT
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENTPHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENT
PHARMACOHORE MAPPING AND VIRTUAL SCRRENING FOR RESEARCH DEPARTMENT
 
3d qsar
3d qsar3d qsar
3d qsar
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
De novo drug design
De novo drug designDe novo drug design
De novo drug design
 
Qsar and drug design ppt
Qsar and drug design pptQsar and drug design ppt
Qsar and drug design ppt
 
CADD
CADDCADD
CADD
 
Computer aided drug designing
Computer aided drug designingComputer aided drug designing
Computer aided drug designing
 
Energy minimization methods - Molecular Modeling
Energy minimization methods - Molecular ModelingEnergy minimization methods - Molecular Modeling
Energy minimization methods - Molecular Modeling
 
Pharmacophore mapping joon
Pharmacophore mapping joonPharmacophore mapping joon
Pharmacophore mapping joon
 
COMPUTER AIDED DRUG DESIGN
COMPUTER AIDED DRUG DESIGNCOMPUTER AIDED DRUG DESIGN
COMPUTER AIDED DRUG DESIGN
 
Pharmacophore Modeling in Drug Designing
Pharmacophore Modeling in Drug DesigningPharmacophore Modeling in Drug Designing
Pharmacophore Modeling in Drug Designing
 

Similar to Ligand based drug design

Quantitative Structure Activity Relationship.pptx
Quantitative Structure Activity Relationship.pptxQuantitative Structure Activity Relationship.pptx
Quantitative Structure Activity Relationship.pptxRadhaChafle1
 
QSAR by hansch analysis
QSAR by hansch analysisQSAR by hansch analysis
QSAR by hansch analysiskholood adil
 
QSAR Studies presentation
 QSAR Studies presentation QSAR Studies presentation
QSAR Studies presentationAshruti agrawal
 
Electronic Parameters used for quantifying Drug - Introduction
Electronic  Parameters used for quantifying Drug - IntroductionElectronic  Parameters used for quantifying Drug - Introduction
Electronic Parameters used for quantifying Drug - IntroductionVisali Kathirvel
 
Insilico methods for design of novel inhibitors of Human leukocyte elastase
Insilico methods for design of novel inhibitors of Human leukocyte elastaseInsilico methods for design of novel inhibitors of Human leukocyte elastase
Insilico methods for design of novel inhibitors of Human leukocyte elastaseJayashankar Lakshmanan
 
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...RamiahValliappan2
 
Lecture5 100717171918-phpapp01
Lecture5 100717171918-phpapp01Lecture5 100717171918-phpapp01
Lecture5 100717171918-phpapp01Cleophas Rwemera
 
Lecture6 100717171815-phpapp01
Lecture6 100717171815-phpapp01Lecture6 100717171815-phpapp01
Lecture6 100717171815-phpapp01Cleophas Rwemera
 
Qsar parameters by ranjeeth k
Qsar parameters by ranjeeth kQsar parameters by ranjeeth k
Qsar parameters by ranjeeth kRanjeethK2
 

Similar to Ligand based drug design (20)

Lecture 6
Lecture 6Lecture 6
Lecture 6
 
QSAR.pptx
QSAR.pptxQSAR.pptx
QSAR.pptx
 
QSAR.pptx
QSAR.pptxQSAR.pptx
QSAR.pptx
 
Qsar UMA
Qsar   UMAQsar   UMA
Qsar UMA
 
Quantitative Structure Activity Relationship.pptx
Quantitative Structure Activity Relationship.pptxQuantitative Structure Activity Relationship.pptx
Quantitative Structure Activity Relationship.pptx
 
QSAR by hansch analysis
QSAR by hansch analysisQSAR by hansch analysis
QSAR by hansch analysis
 
QSAR Studies presentation
 QSAR Studies presentation QSAR Studies presentation
QSAR Studies presentation
 
Lecture 5
Lecture 5Lecture 5
Lecture 5
 
opti.pptx
opti.pptxopti.pptx
opti.pptx
 
Qsar studies
Qsar studiesQsar studies
Qsar studies
 
Qsar parameter
Qsar parameterQsar parameter
Qsar parameter
 
Electronic Parameters used for quantifying Drug - Introduction
Electronic  Parameters used for quantifying Drug - IntroductionElectronic  Parameters used for quantifying Drug - Introduction
Electronic Parameters used for quantifying Drug - Introduction
 
QSAR.pptx
QSAR.pptxQSAR.pptx
QSAR.pptx
 
Insilico methods for design of novel inhibitors of Human leukocyte elastase
Insilico methods for design of novel inhibitors of Human leukocyte elastaseInsilico methods for design of novel inhibitors of Human leukocyte elastase
Insilico methods for design of novel inhibitors of Human leukocyte elastase
 
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...
Unit –II : Chemical Dynamics Potential energy surfaces, Kinetic isotopic effe...
 
Med chem unit2
Med chem unit2Med chem unit2
Med chem unit2
 
Lecture5 100717171918-phpapp01
Lecture5 100717171918-phpapp01Lecture5 100717171918-phpapp01
Lecture5 100717171918-phpapp01
 
QSAR
QSARQSAR
QSAR
 
Lecture6 100717171815-phpapp01
Lecture6 100717171815-phpapp01Lecture6 100717171815-phpapp01
Lecture6 100717171815-phpapp01
 
Qsar parameters by ranjeeth k
Qsar parameters by ranjeeth kQsar parameters by ranjeeth k
Qsar parameters by ranjeeth k
 

Recently uploaded

SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxkessiyaTpeter
 
Recombinant DNA technology( Transgenic plant and animal)
Recombinant DNA technology( Transgenic plant and animal)Recombinant DNA technology( Transgenic plant and animal)
Recombinant DNA technology( Transgenic plant and animal)DHURKADEVIBASKAR
 
FREE NURSING BUNDLE FOR NURSES.PDF by na
FREE NURSING BUNDLE FOR NURSES.PDF by naFREE NURSING BUNDLE FOR NURSES.PDF by na
FREE NURSING BUNDLE FOR NURSES.PDF by naJASISJULIANOELYNV
 
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptx
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptxBREEDING FOR RESISTANCE TO BIOTIC STRESS.pptx
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptxPABOLU TEJASREE
 
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.PraveenaKalaiselvan1
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPirithiRaju
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander  in real timeGrafana in space: Monitoring Japan's SLIM moon lander  in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real timeSatoshi NAKAHIRA
 
Environmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial BiosensorEnvironmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial Biosensorsonawaneprad
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024AyushiRastogi48
 
Neurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trNeurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trssuser06f238
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Patrick Diehl
 
Harmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms PresentationHarmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms Presentationtahreemzahra82
 
TOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsTOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsssuserddc89b
 
Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptArshadWarsi13
 
Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2John Carlo Rollon
 
Behavioral Disorder: Schizophrenia & it's Case Study.pdf
Behavioral Disorder: Schizophrenia & it's Case Study.pdfBehavioral Disorder: Schizophrenia & it's Case Study.pdf
Behavioral Disorder: Schizophrenia & it's Case Study.pdfSELF-EXPLANATORY
 
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfAnalytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfSwapnil Therkar
 
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝soniya singh
 
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |aasikanpl
 

Recently uploaded (20)

SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptxSOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
SOLUBLE PATTERN RECOGNITION RECEPTORS.pptx
 
Recombinant DNA technology( Transgenic plant and animal)
Recombinant DNA technology( Transgenic plant and animal)Recombinant DNA technology( Transgenic plant and animal)
Recombinant DNA technology( Transgenic plant and animal)
 
FREE NURSING BUNDLE FOR NURSES.PDF by na
FREE NURSING BUNDLE FOR NURSES.PDF by naFREE NURSING BUNDLE FOR NURSES.PDF by na
FREE NURSING BUNDLE FOR NURSES.PDF by na
 
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptx
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptxBREEDING FOR RESISTANCE TO BIOTIC STRESS.pptx
BREEDING FOR RESISTANCE TO BIOTIC STRESS.pptx
 
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander  in real timeGrafana in space: Monitoring Japan's SLIM moon lander  in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real time
 
Environmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial BiosensorEnvironmental Biotechnology Topic:- Microbial Biosensor
Environmental Biotechnology Topic:- Microbial Biosensor
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024
 
Neurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 trNeurodevelopmental disorders according to the dsm 5 tr
Neurodevelopmental disorders according to the dsm 5 tr
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?
 
Harmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms PresentationHarmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms Presentation
 
TOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physicsTOPIC 8 Temperature and Heat.pdf physics
TOPIC 8 Temperature and Heat.pdf physics
 
Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.ppt
 
Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2
 
Behavioral Disorder: Schizophrenia & it's Case Study.pdf
Behavioral Disorder: Schizophrenia & it's Case Study.pdfBehavioral Disorder: Schizophrenia & it's Case Study.pdf
Behavioral Disorder: Schizophrenia & it's Case Study.pdf
 
Volatile Oils Pharmacognosy And Phytochemistry -I
Volatile Oils Pharmacognosy And Phytochemistry -IVolatile Oils Pharmacognosy And Phytochemistry -I
Volatile Oils Pharmacognosy And Phytochemistry -I
 
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfAnalytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
 
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Munirka Delhi 💯Call Us 🔝8264348440🔝
 
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
 

Ligand based drug design

  • 2.  Quantitative Structure Activity Relationship(QSAR) is a set of methods that tries to find a mathematical relationship between a set of descriptors of molecules and their activity.  The descriptors can be experimentally or computationally derived. Using regression analysis, one can extract a mathematical relationship between chemical descriptors and activity.
  • 3.  The QSAR approach attempts to identify and quantify the physicochemical properties of a drug and to see whether any of these properties has an effect on the drug‘s biological activity.  such a relationship holds true, an equation can be drawn up which quantifies the relationship and allows the medicinal chemist to say with some confidence that the properties has an important role in the distribution or mechanism of the drug.  It also allows the medicinal chemist some level of prediction. By quantifying physicochemical properties, it should be possible to calculate in advance what the biological activity of a novel analogue might be.
  • 4.  The formulation of thousands of equations using QSAR methodology attests to a validation of its concepts and its utility in the elucidation of the mechanism of action of drugs at the molecular level and a more complete understanding of physicochemical phenomena such as hydrophobicity.  Crum-Brown and Fraser expressed that the physiological action of a substance was a function of its chemical composition and constitution.
  • 5. 1. Allows the medicinal chemist to target efforts on analogues which should have improved activity and thus, decreases the number of analogues which have to be made. 2. If an analogue is discovered which does not fit the equation, it implies that some other feature is important and provides a lead for further development.
  • 6.  They refer to any structural, physical, or chemical property of a drug.  The drug will have a large number of such properties and it would be a very difficult task to quantify and relate them all to biological activity at the same time.  A simple more practical approach is to consider one or two physicochemical properties of the drug and to vary these while attempting to keep other properties constant. This is not as simple as it sounds, since it is not always possible to vary one property without affecting another.  The QSAR study then considers how the hydrophobic, electronic, and steric properties of the substituents affect biological activity.
  • 7.
  • 8.  Hydrophobic character of a drug is crucial to how easily it crosses cell membranes and may also be important in receptor interactions. Changing substituents on a drug may well have significant effects on its hydrophobic character and hence its biological activity.  The partition coefficient (P) p=Concentration of drug in octanol/Concentration of drug in aqueous solution  The hydrophobic character of a drug can be measured experimentally by testing the drug's relative distribution in an octanol/water mixture. Hydrophobic molecules will prefer to dissolve in the octanol layer of this two-phase system, whereas hydrophilic molecules will prefer the aqueous layer. The relative distribution
  • 9.  Hydrophobic compounds will have a high P value, whereas hydrophilic compounds will have a low P value.  By plotting these P values against the biological activity of these drugs, it is possible to see if there is any relationship between the two properties.  The graph is drawn by plotting log (1/C) versus log P.
  • 10.  The scale of numbers involved in measuring C and P usually covers several factors of ten and so the use of logarithms allows the use of more manageable numbers.  Where the range of the logP values is restricted to a small range (e.g. logP = 1-4), a straight-line graph is obtained showing that there is a relationship between hydrophobicity and biological activity. Such a line would have the following equation: log (1/C) =k1 log p+k2
  • 11.  For example, the binding of drugs to serum albumin is determined by their hydrophobicity and a study of 40 compounds resulted in the following equation: log (1/C) = 0.75 log P + 2.30  The equation shows that serum albumin binding increases as log P increases. In other” words, hydrophobic drugs bind more strongly to serum albumin than hydrophilic drugs.  As an example of this, the cardiotonic agent was found to produce 'bright visions' in some patients, which implied that it was entering the CNS.
  • 12.  This was supported by the fact that the log P value of the drug was 2.59. In order to prevent the drug entering the CNS, the 4-OMe group was replaced with a 4-S(O)Me group. This particular group is approximately the same size as the methoxy group, but more hydrophilic. The logP value of the new drug (sulmazole) was found to be 1.17. The drug was now to hydrophilic to enter the CNS and was free of CNS side-effects.
  • 13.
  • 14.  The electronic effects of various substituents will clearly have an effect on a drug‘s ionization or polarity. This in turn may have an effect on how easily a drug can pass through cell membranes or how strongly it can bind to a receptor.  As far as substituents on an aromatic ring are concerned, the measure used is known as the Hammett substitution constant .  The Hammett substitution constant ( ) is a measure of the electron withdrawing or electron donating ability of a substituent and has been determined by measuring the dissociation of a series of substituted benzoic acids compared to the dissociation of benzoic acid itself.
  • 15.  Benzoic acid is a weak acid and only partially ionizes in water. An equilibrium is set up between the ionized and non-ionized forms.  Dissociation constant (KH)=[PhCO0¯]/[PhCO0H] Ionization of benzoic acid
  • 16.  When a substituent is present on the aromatic ring, this equilibrium is affected. Electron withdrawing groups, such as a nitro group, result in the aromatic ring having a stronger electron withdrawing and stabilizing influence on the carboxylate anion.  The equilibrium will therefore shift more to the ionized form such that the substituted benzoic acid is a stronger acid and has a larger Kx value.
  • 17.  If the substituent X is an electron donating group such as an alkyl group, then the aromatic ring is less able to stabilize the carboxylate ion. The equilibrium shifts to the left and a weaker acid is obtained with a smaller Kx value.  The Hammett substituent constant ( ) for a particular substituent (X) is defined by the following equation: x = log Kx/KH = log Kx - log KH  Benzoic acids containing electron withdrawing substituents will have larger KX values than benzoic acid itself (KH) and therefore the value of x for an electron withdrawing substituent will be positive. Substituents such as Cl, CN, or CF3 have positive a values.
  • 18.  Benzoic acids containing electron donating substituents will have smaller Kx values than benzoic acid itself and hence the value of x for an electron donating substituent will be negative.  Substituents such as Me, Et, and Bu' have negative a values. The Hammett substituent constant for H will be zero.  The Hammett constant takes into account both resonance and inductive effects. Therefore, the value of a for a particular substituent will depend on whether the substituent is meta or para.
  • 19.  These are absolutely necessary for a drug molecule to engage into a viable interaction either with a drug receptor or with an enzyme.  Obviously, this essentially demands certain specific criteria that a drug molecule must fulfil as: bulk, size and shape of the drug. The bulky substituent more or less serve as a shield that eventually hinders the possible and feasible interaction taking place between a drug and a receptor.  The quantitation of steric effects is complex at best and challenging in all other situations, particularly at the molecular level.
  • 20.  An added level of confusion comes, when attempts are made to delineate size and shape. Nevertheless, sterics are of overwhelming importance in ligand-receptor interactions as well as in transport phenomena in cellular systems. The first steric parameter to be quantified and used in QSAR studies was Taft's(Es) constant. Es is defined as,  where k, and k, represent the rates of acid hydrolysis of esters, XCH2COOR and CH3COOR, respectively. Es = log(Kx/KH)
  • 21.  Molar refractivity (MR)is usually designated as a simple measure of the volume occupied either by an individual atom or a cluster (group) of atoms.  It is one of the most widely used steric parameter. where, n = Index of refraction, MW = Molecular Weight, d = Density, MW/d = Volume and n²–1/n²+2 =Correction factor (i.e., how easily the substituent can undergo polarization)  Molar refractivity is specifically significant in a situation when the substituent possesses either π electron or lone pairs of electrons.
  • 22.  It incorporate a Polarizability component that may describe cohesion and is related to London dispersion forces.  The failure of the MR descriptor to adequately address three-dimensional shape issues led to Verloop‘s development of sterimol parameters.
  • 23.  The unique revelation and wisdom of a latest computer researched programme termed as sterimol has indeed helped a long way in measuring the steric factor to a reasonably correct extent. It essentially aids in the calculation of desired steric substituent values (Verloop steric parameters) based on various standard physical parameters, such as : Vander Waals radii, bond lengths, bond angles, and ultimately the proposed most likely conformations for the substituent under examination.  It is, however, pertinent to mention here that unlike the Taft’s steric factor (ES) the Verloop steric parameters may be measured conveniently and accurately for any substituent.
  • 24. Verloop Steric Parameters for a Carboxylic Acid (— COOH) Moiety Example : Carboxylic acid (Benzoic Acid) : The ensuing Verloop steric parameters for a carboxylic acid moiety are duly measured, where L represents the length of the substituent, and B1 – B4 designate the radii (i.e., longitudinal and horizontal) of the two functional groups viz., carboxyl and hydroxyl (—O—H).

Editor's Notes

  1. Example : Carboxylic acid (say, Benzoic Acid) : The ensuing Verloop steric parameters for a carboxylic acid moiety are duly measured as shown in Fig. 2.6 below, where L represents the length of the substituent, and B1 – B4 designate the radii (i.e., longitudinal and horizontal) of the two functional groups viz., carboxyl and hydroxyl (—O—H).