1.
Bioinformatic in drug
designing
Presented by,
SALMAN KHAN
SREERAM PRAKASH
SAHIL DOGRA
PRATYASHA GOGOI

2.
What is bioinformatics?
• The field of science in which biology, computer science,
and information technology merge into a single discipline.
• The ultimate goal of the field is to enable the discovery of new biological insights
as well as to create a global perspective from which unifying principles in
biology can be discerned.
• There are three important sub- disciplines within bioinformatics:
the development of new algorithms and statistics with which to assess
relationships among members of large data sets;
the analysis and interpretation of various types of data including nucleotide and
amino acid sequences, protein domains, and protein structures; and
 the development and implementation of tools that enable efficient access and
management of different types of information.

3.
Drug Design
• In pharmacology, Drug is any chemical agent that
alters the biochemical or physiological processes of tissues or organisms
• Drug design or rational drug design, is the discovery process of finding new
medications based on the knowledge of a biological target.
• The drug is most commonly an organic small molecule that activates or
inhibits the function of a biomolecule such as a protein, which in turn results
in a therapeutic benefit to the patient & it is mostly involves the design of
molecules that are complementary in shape and charge to the biomolecular
target with which they interact & therefore will bind to it & drug design
frequently but not necessarily relies on computer modeling technique.

4.
Developing a new drug is
 Lengthy,
 Risky It involves a number of processes
 Very expensive.
Drug discovery development and AIM
 Identify disease
 Isolate protein involved in disease (2-5 years)
 Find a drug effective against disease protein (2-5 years)
 Preclinical testing (1-3 years) Scale-up: using animal studies, formulation;
 Human clinical trails(2-10 years)
 FDA approval (2-3 years)
 Drug.
Aim:
 The diagnosis- determine the cause of disease.
 Cure- relieve of the symptoms of a disease.
 Migration –action of reducing the severity of a disease.
 Treatment- Medical care.
 Prevention of disease.

5.
Technology is impacting this process

6.
Application of bioinformatics in drug discovery
The application of bioinformatics cut across all the
process of drug discovery, thereby
 Reducing the risk of drug failure
 Making it a bit cheaper
 Reducing the time spent in the discovery and
 Also automates the entire process, thereby
reducing human intervention.

7.
High- Throughput Screening
 Drug companies now have millions of samples of chemical compounds.
 High-throughput screening can test 100,000 compounds a day for activity against a protein target.
 Maybe tens of thousands of these compounds will show some activity for the protein.
 The chemist needs to intelligently select the 2 - 3 classes of compounds that show the most promise for being
drugs to follow.

8.
Computer Aided Drug Designing
• Machine Learning Methods E.g. Neural nets, Bayesian
nets, SVMs, Kahonen nets.
• Train with compounds of known activity ◦
• Predict activity of “unknown” compounds
• Scoring methods
• Profile compounds based on properties related to
target
• Fast Docking ◦ Rapidly “dock” 3D representations of
molecules into 3D representations of proteins, and score
according to how well they bind.

9.
Gene chips
“Gene chips” allow us to look for changes in
protein expression for different people with a
variety of conditions, and to see if the presence
of drugs changes that expression .
Makes possible the design of drugs to target
different phenotypes compounds administered
people / conditions e.g. obese, cancer, Caucasian
expression profile (screen for 35,000 genes).

10.
Molecular Modeling
• 3D Visualization of
interactions between
compounds and proteins.
• “Docking” compounds into
proteins computationally .

11.
3-D visualization
• X-ray crystallography and NMR Spectroscopy can
reveal 3D structure of protein and bound
compounds .
• Visualization of these “complexes” of proteins and
potential drugs can help scientists understand the
mechanism of action of the drug and to improve
the design of a drug .
• Visualization uses computational “ball and stick”
model of atoms and bonds, as well as surfaces .
• Stereoscopic visualization is available.

12.
In vitro& in silico ADME models
 Traditionally, animals were used for pre human
testing. However,
 Animal tests are expensive, time consuming and
ethically undesirable.
 ADME(absorption, distribution, metabolism,
excretion)techniques help model how drug will
likely act in body.
 These methods can be experimental(in
vitro)using cellular tissue, or in silico, using
computational models

13.
In silico ADME models
• Computational methods can predict compound properties important to
ADME .e.g.
Log p.a lipophilicity measure
Solubility
Permeability
Cytochrome p450 metabolism

14.
Structure database
• MSD: The macromolecular structure database
• 3Dseq:3d sequence alignment server
• FSSP: based on exhaustive all against all 3d structure comparison
of protein in the PDB
• DALI: fold classification based on structure – structure
• NDB: nucleic acid structure database

15.
• SWISS-PROT: Annotated Sequence Database
• TrEMBL: Database of EMBL nucleotide translated sequences
• InterPro: Integrated resource for protein families, domains and
functional sites.
• SWISS-PROT, TrEMBL, Ensembl and RefSeq.
• IntEnz: The Integrated relational Enzyme database (IntEnz) will
contain enzyme data approved by the Nomenclature Committee
Protein databases

16.
References
• Https://www.Xenotech.Com/nonclinical-studies/adme/
• Https://blogs.Siliconindia.Com/shribioinformatics/bioinformatics
-in-drug-designing-bid-ch0s2jv471570920.Html
• Bioinformatics approaches for new drug discovery: a review
kullappan malathi et,al.2016.