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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