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Molecular Modelling and Insilico Drug Design
Introduction to molecular modelling,
Protein modelling using High Throughput methods
Modelling of targets and receptors.
Virtual Library design,
vHTS
Virtual screening (VS) is a computational technique used
in drug discovery to search libraries of small molecules in order
to identify those structures which are most likely to bind to
a drug target, typically a protein receptor or enzyme.
HTS is brute force experimental method in which thousands,
sometimes millions of compounds are screened robotically.
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Insilico drug design types Ligand based strategy
In LBVS process, the most effective biologically active lead
molecule is detected using structural or topological similarity or
pharmacophoric similarity search.
The leads generated are ranked based on their similarity score,
obtained using different methods or algorithms.
There are 5 classes of LBVS.
Small Molecule Alignment
In small molecular alignment the detection of similarity is
carried out by superposing each of the test molecules of the
database with the reference molecule, and based on their extent
of similarity they are ranked.
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Generally in the superposition process the test molecule is taken
as flexible, and the reference molecule can be rigid or flexible.
ToolsFlexS, GASP, MEP, MIMIC, fFlash.
Descriptor Based Screening
The molecular descriptor is generated based on different
location specific molecular details that are conformational,
topological or microscopic information.
Based on the dimension of the properties, descriptors can be
grouped into various classes like 1D-, 2D- and 3D- descriptors.
The descriptors may be linear, scalar or nonlinear.
1D- and 2D- Descriptors
Bulk properties like Molecular weight, Molar refractivity, log P
are in general considered as 1D descriptors of a molecule where
as 2D descriptors are generated based on different two-
dimensional qualitative or quantitative properties of lead
molecule.
Binary Descriptor
In binary descriptor representation, the presence of structuralproperties for each position of lead molecule is narrated by
means of a Boolean bit set to one otherwise to zero.
ToolsMACCS (Structural keys), Daylight fingerprint
(Molecular fingerprint).
Real-Value Descri ptor
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The real value descriptor vectors represent the pharmacophoric
site of a lead compound by generating a hologram.
ToolsMAD
Feature Tree
A feature tree is a node labeled, unrooted tree, where in different
nodes of the tree represent the functional groups of the molecule
with their physicochemical properties and edges connect nodes
as in the chemical structure.
ToolsFTree, MTree, NIPALSTREE.
3D Descriptors
3D similarity search is based on the concept that molecule with
similar conformational features shows similar biological
activity.
The estimation of similarity in descriptor based analysis is also
based on different framework of descriptor (3D descriptors) and
the different coefficients used in this search procedure.
Tools3D feature, Disco.
Scaffold Hopping,
Scaffold Hopping
Scaffold hopping is a recently developed advanced similarity
searching procedure.
During the screening process, molecules are searched with
similar bioactivity to a reference ligand, but with different
molecular frameworks.
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This method involves the technique of searching the compounds
with similarity in terms of chemical, pharmacological and
biological properties.
ToolsMolprint, FEPOPS, CATS.
The aim of scaffold hopping is to discover structurally novel
compounds starting from known active compounds by
modifying the central core structure of the molecule.
Their application has led to several molecules with chemically
completely different core structures, and yet binding to the same
receptor.
Computational approaches for scaffold hopping highlight the
challenges of the field that are still unsolved.
This approach requires the availability of a template a
chemical structure displaying the desired biological activity, and
it is based on the assumption that the same biological activity
can be exerted by other compounds that maintain some essential
features of the template but are structurally different otherwise.
Pharmacophore Similarity Search
In pharmacophoric similarity search, the conformational andelectrostatic properties of lead molecule that are necessary for
the optimum interaction with target site are considered.
This similarity search is based on the phenomenon of ligand-
target binding.
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Various statistical methods are used for analysis of
pharmacophoric patterns and to asses the biological activity of
ligands.
ToolsGRID, BRUTUS.
Recursive Partitioning
The recursive partitioning approach divides the data set and
arrange using Decision Tress containing a single or multiple
descriptors at each node.
ToolsCerBeruS, MCASE, PGLT
Graph Based Simi larity Assessment
Graph based analysis is based on representing the conformer
using graph.
Usually it identifies the most common conformational feature
known as Maximum Common Conformation (MCS).
This technique is bit more time taking than descriptor based
finger print techniques.
ToolsRASCAL.
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Structural Targets3D structure of target receptors determined by
X-ray crystallography
NMR
Homology modeling
Protein Data Bank
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Archive of experimentally determined 3D structures of biological
macromolecules
Virtual Screening... when target structure is
unknown
Virtual High Throughput Screening
Advantages Less expensive than High Throughput Screening
Faster than conventional screening
Scanning a large number of potential drug like molecules in
very less time.
HTS itself is a trial and error approach but can be better
complemented by virtual screening.
pharmacophore mapping, Pharmacophore model
Set of points in space defining the binding of ligands with
target.
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Key factors in developing such a model are the
determination of functional groups essential for binding,
their correspondence from one ligand to another, and the
common spatial arrangement of these groups when bound
to the receptor
Pharmacophore Features
HB Acceptor & HB Donor Hydrophobic
Hydrophobic aliphatic
Hydrophobic aromatic
Positive charge/Pos. Ionizable
Negative charge/Neg. Ionizable
Ring Aromatic
Each feature consists of four parts:
1. Chemical function
2. Location and orientation in 3D space
3. Tolerance in location
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4. Weight
Pharmacophore mapping
It is a 3D description of a pharmacophore, developed by
specifying the nature of the key pharmacophoric features and
the 3D distance map among all the key features.
A Pharmacophore map can be generated by superposition of
active compounds to identify their common features.
Based on the pharmacophore map either de novodesign or 3D
database searching can be carried out.
Lead Optimization.
Drug Discovery overview (LI & LO)
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Lead discovery- Identification of a compound that triggers
specific biological actions.
Lead optimization- Properties of the lead are tested with
biological assays; new molecules are designed and synthesized
to obtain the desired properties
LEAD OPTIMISATION
It is the process of finding a compound that has an
advantage over a related lead.
Better understanding of physical and chemical
determinants.
Undesirable side effects
Experimental verification of positional requirements of
drug - receptor binding.
Improved ADME properties.
Lesser toxicity.
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Quantitative Structure Activity Relationships (QSAR)
QSARs are the mathematical relationships linking chemical
structures with biological activity using physicochemical or any
other derived property as an interface.
Biological activity = f (Physico-chemical properties)
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.
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Various descriptors like molecular weight, number of rotatable
bonds LogP etc. are commonly used.
Many QSAR approaches are in practice based on the data
dimensions.
It ranges from 1D QSAR to 6D QSAR.
Types of QSARs
Two Dimensional QSAR
- Classical Hansh Analysis
- Two dimensional molecular properties
Three Dimensional QSAR
- Three dimensional molecular properties
- Molecular Field Analysis
- Molecular Shape Analysis
- Distance Geometry
- Receptor Surface Analysis
The PLS results are presented as contour plots
Steric Bulk:
Green = Steric Favourable
Yellow = Steric Unfavourable
Electrostatics:
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Red = Electronegative Favourable
Blue = Electronegative Unfavourable
QSAR Generation Process
1.Selection of training set
2. Enter biological activity data
3. Generate conformations
4. Calculate descriptors
5. Selection of statistical method
6. Generate a QSAR equation
7. Validation of QSAR equation
8. Predict for Unknown
Descriptors
1.Structural descriptors
2.Electronic descriptors
3.Quantum Mech. descriptors
4.Thermodynamic descriptors
5.Shape descriptors
6.Spatial descriptors
7.Conformational descriptors
8. Receptor descriptors
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Selection of Descriptors
1.What is particularly relevant to the therapeutic target?
2.
What variation is relevant to the compound series?
3.What property data can be readily measured?
4.What can be readily calculated?
Predictive Science (BiologicalActivity, ADMET).
Traditional Approach
Discovery & development of new drug : long, labour -demanding
process ; multi-step ; invivobiological screens.
Average time to discover, develop and approve a drug - 8 to 15
years.
Reasons of failure :
Selection of improper targets.
Poor pharmacokinetics, side effects.
3. Poor toxicological and safety related pharmacological properties.
4. Elongated discovery and development time course
Insilico Approach
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The possible study of hypothetical compounds; their low cost; and the
fact that such virtual experiments are typically based on human data.
In pharmacology, biological activity or pharmacological
activity describes the beneficial or adverse effects of a drug on living
matter.
Activity depends on-active ingredient or pharmacophore.
Activity depends critically on fulfilment of the ADME criteria.
Drug Absorption : The passage of the drug from its site of administration
into the systemic circulation.
Drug Distribution : After absorption of the drug, it is usually distributed
into different tissues & the body fluid compartments such as plasma,
extracellular fluid, intracellular fluid.
It mainly depends on its physiochemical properties.
Drug metabolism : Also known as xenobiotic metabolism is
the biochemical modification of pharmaceutical substance or xenobiotics
respectively by living organisms , usually through specialized enzymatic
systems. (Biotransformation)
Drug metabolism often converts lipophilic chemical compounds into
more readily excreted hydrophilic products.
The rate of metabolism determines the duration and intensity of a drug's
pharmacological action.
Drug Excretions : Removal of drug compounds from the body.
Routes of excretion : Bile, Urine, Feces, Expired air, Sweat, Saliva, Milk.
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Drug Toxicity : Also called adverse drug reaction(ADR) is
manifestations of the adverse effects of drugs administered
therapeutically or in the course of diagnostic techniques.
Lipinskis Rule of Five
An ideal drug has not more than one violation of the following criteria:
1.
Not more than 5 hydrogen bond donors .
2.
Not more than 10 hydrogen bond acceptors.
3.
A molecular mass less than 500 daltons.
4.
An octanol-water partition coefficient (logP)not greater than 5.
Physiochemical Properties
LipophilicityIs measured interms of partition coefficient log P in anoctanol/water system.
LogP = Log[Co ]/[Cw].
LogP > 2 - lipophilic drug.
LogP < 2 - hydrophilic drug.
2. Solubility
Is a critical factor; drug has to be dissolved before they can be absorbed.
Solubility and rate of dissolution are very imp factors.
3. Ionisation/ Dissociation Constant (pKa)
Quantitative measure of strength of an acid in solution. pKa= -log10Ka
Only the unionised form of a drug can partition across biological
membranes.
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The ionised form tends to be more water soluble [required for drug
administration and distribution in plasma].
4. Permeability
Is predicted through Caco-2 cells.
They serve as a model for human intestinal absoption.
Data are expressed as apparent permeability coefficients (Papp, cm/sec)
given by :
Papp(cm/sec)= amt transported/(area*initial concentration *time)
Software : Gastro Plus, iDEA.
5. Hydrogen Bonding
H2bonding is imp to determinant of permeability.
Calculated using parameters like free energy factors and polar surface
area (PSA).
5. Blood Brain Barrier permeability
Drugs that act in CNS need to cross BBB to reach molecular target.
Molecules with a mol mass < 450 Da or with PSA
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11. Half Life (t1/2)
Time taken for a drug conc. in the plasma to reduce by 50%
t1/2= 0.693 Vd/Cl
12.Polar Surface Area(PSA)
Is defined as amount of molecular surface (vander-walls) arising from
polar atoms (nitrogen and oxygen atom together with attached hydrogens)
.
PSA used in the prediction of oral absorbtion, brain penetration, intestinal
absorption, Caco-2- permeability
Metabolism Stability Prediction
Prediction is based on physicochemical properties and knowledge of the
structure of enzyme and mechanism of action.
Descriptors include : Molecular sites sensitive to oxidation or
conjugation, 3-D structure of the chemical, steric hindrance, molecular
surface properties chemical properties, quantum mechanics, polarity,
hydrophobicity, liphophilicity, hydrogen bonding capacities,3D
molecular interactions etc.
Models for predicting ADME
QSAR(Quantitative structure-activity Relationship)
QSAR is a mathematical relationship between a biological activity of a
molecular system and its geometric and chemical characteristics.
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A general formula for a quantitative structure-activity relationship
(QSAR) can be given by the following:
activity = f (molecular or fragmental properties)
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.
Prediction of intestinal permeability, blood brain barrier permeability can
be done using QSAR.
Several quantitative descriptors based on 2D or 3D molecular structures
have been used like fragment descriptor, log P, H2 bonding ,PSA &
quantum chemical parameters.
In addition to study the relationship multiple linear regression, partial
least squares, artificial neural network is used.
Prediction of active transport process is done by comparative molecular
field analysis (CoMFA).
CoMFA- is a representative 3D QSAR approach.
It explains the gradual changes in observed biological properties by
evaluating electrostatic & steric (Vander walls interactions) fields at
regularly spaced grid points surrounding a set of mutually aligned
ligands.
Prediction of oral bio availability by Generalised Regression neural
network (GRNN).
Prediction of metabolic stability using k-nearest neighbor method.
Admet Descriptors Calculation Tools
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PreADMET http://preadmet.bmdrc.org/
Molecular Descriptors Calculation - 1081 diverse molecular descriptors
Drug-Likeness Prediction - Lipinski rule, lead-like rule, Drug DB like
rule
ADME Prediction - Caco-2, MDCK, BBB, HIA, plasma protein
binding and skin permeability data.
Toxicity Prediction - Ames test and rodent carcinogenicity assay
SPARC Online Calculator http://ibmlc2.chem.uga.edu/sparc/
SPARC on-line calculator for prediction of pKa, solubility,
polarizability, and other properties.
Daylight Chemical Information Systems
www.daylight .com/ daycgi/clogp
Calculation of log P by the CLOGP algorithm from BioByte; also has
access to the LOGPSTAR database of experimental log P data .
Molinspiration Cheminformatics
www.molinspiration.com/seruices/index.
Calculation of molecular properties relevant to drug design and QSAR,
including log P, polar surface area, rule of five parameters, and drug-
likeness index.
Pirika- www.pirika.com
Calculation of various types of molecular properties, including boiling
point, vapor pressure, and solubility; web demo restricted to only
aliphatic molecules.
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Actelion -www.actelion.com/page/property_explorer
Calculation of molecular weight, logP, solubility, drug-score and
toxicity risk .
Virtual Computational Chemistry Laboratory
www. vcclab. org
Prediction of log P and water solubility based on associative neural
networks as well as other parameters; comparison of various prediction
methods.
Structural Mining Protein Ligand work analysis.
Studyof drug-interactions and Docking.
Docking
Docking refers to a computational scheme that
tries to find the best binding orientation between
two biomolecules where the starting point is the
atomic coordinates of the two molecules
Additional data may be provided (biochemical,
mutational, conservation, etc.) and this can
significantly improve the performance, however
this extra information is not required
DOCK The DOCK program is from the Kuntz group at
UCSF
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It was the first docking program developed in
1982
It represents the (negativeimage of the) binding
site as a collection of overlapping spheres
CLIX
CLIX uses a chemical description of the
receptor and distance constraints on the
atoms