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Presented by :-
Saurabh Verma
M.S.Pharm-First year
Department
Pharmacoinformatics
Important Points in Drug Design based on
Bioinformatics Tools
Chemical Modification of Known Drugs
 Drug improvement by chemical modification
 Pencillin G -> Methicillin -> morphine->nalorphine
Receptor Based drug design
 Receptor is the target (usually a protein)
 Drug molecule binds to cause biological effects
 It is also called lock and key system
 Structure determination of receptor is important
Ligand-based drug design
 Search a lead compound or active ligand
 Structure of ligand guide the drug design process
Overview Continued –
A simple example
Protein
Small molecule
drug
Overview Continued –
A simple example
Protein
Small molecule
drug
Protein
Protein
disabled …
disease
cured
Chemoinformatics
ProteinSmall molecule
drug
Bioinformatics
•Large databases •Large databases
Chemoinformatics
ProteinSmall molecule
drug
Bioinformatics
•Large databases
•Not all can be drugs
•Large databases
•Not all can be drug targets
Chemoinformatics
ProteinSmall molecule
drug
Bioinformatics
•Large databases
•Not all can be drugs
•Opportunity for data
mining techniques
•Large databases
•Not all can be drug targets
•Opportunity for data
mining techniques
Important Points in Drug Design based on
Bioinformatics Tools
Application of Genome
3 billion bases pair
20,000 unique genes
Any gene may be a potential drug target
~500 unique target
Their may be 10 to 100 variants at each target gene
1.4 million SNP
Drug Discovery & Development
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)
Formulation
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)
FileIND
File
N
DA
Techology is impacting this process
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
1. High-Throughput Screening
Screening perhaps millions of compounds in a corporate
collection to see if any show activity against a certain disease
protein
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 protei
The chemist needs to intelligently select the 2 - 3
classes of compounds that show the most promise for
being drugs to follow-up
2. Computational Models of Activity
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
3. Combinatorial Chemistry
By combining molecular “building blocks”, we
can create very large numbers of different
molecules very quickly.
Usually involves a “scaffold” molecule, and sets of
compounds which can be reacted with the
scaffold to place different structures on
“attachment points”.
4. Molecular Modeling
• 3D Visualization of interactions between compounds and proteins
• “Docking” compounds into proteins computationally
5.3D 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 available
“Docking” compounds into proteins
computationally
6.In Silico ADME Models
Computational methods can predict compound
properties important to ADME, e.g.
LogP, a liphophilicity measure
Solubility
Permeability
Cytochrome p450 metabolism
Means estimates can be made for millions of
compouds, helping reduce “atrittion” – the failure
rate of compounds in late stage
Reference
An introduction to cheminformatics:-
A.R.Leach
Cheminformatics:-
Johann Gasteiger and Thomas Engel
Molecular modeling-principles and Application:-
A.R.Leach
DRUG DESIGN BASED ON BIOINFORMATICS TOOLS

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DRUG DESIGN BASED ON BIOINFORMATICS TOOLS

  • 1. Presented by :- Saurabh Verma M.S.Pharm-First year Department Pharmacoinformatics
  • 2. Important Points in Drug Design based on Bioinformatics Tools Chemical Modification of Known Drugs  Drug improvement by chemical modification  Pencillin G -> Methicillin -> morphine->nalorphine Receptor Based drug design  Receptor is the target (usually a protein)  Drug molecule binds to cause biological effects  It is also called lock and key system  Structure determination of receptor is important Ligand-based drug design  Search a lead compound or active ligand  Structure of ligand guide the drug design process
  • 3. Overview Continued – A simple example Protein Small molecule drug
  • 4. Overview Continued – A simple example Protein Small molecule drug Protein Protein disabled … disease cured
  • 6. Chemoinformatics ProteinSmall molecule drug Bioinformatics •Large databases •Not all can be drugs •Large databases •Not all can be drug targets
  • 7. Chemoinformatics ProteinSmall molecule drug Bioinformatics •Large databases •Not all can be drugs •Opportunity for data mining techniques •Large databases •Not all can be drug targets •Opportunity for data mining techniques
  • 8. Important Points in Drug Design based on Bioinformatics Tools Application of Genome 3 billion bases pair 20,000 unique genes Any gene may be a potential drug target ~500 unique target Their may be 10 to 100 variants at each target gene 1.4 million SNP
  • 9. Drug Discovery & Development 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) Formulation Human clinical trials (2-10 years) Scale-up FDA approval (2-3 years) FileIND File N DA
  • 10. Techology is impacting this process Identify disease Isolate protein Find drug Preclinical testing GENOMICS, PROTEOMICS & BIOPHARM. HIGH THROUGHPUT SCREENING MOLECULAR MODELING VIRTUAL SCREENING COMBINATORIAL CHEMISTRY IN VITRO & IN SILICO ADME MODELS Potentially producing many more targets and “personalized” targets Screening up to 100,000 compounds a day for activity against a target protein Using a computer to predict activity Rapidly producing vast numbers of compounds Computer graphics & models help improve activity Tissue and computer models begin to replace animal testing
  • 11. 1. High-Throughput Screening Screening perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein
  • 12. 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 protei The chemist needs to intelligently select the 2 - 3 classes of compounds that show the most promise for being drugs to follow-up
  • 13. 2. Computational Models of Activity 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
  • 14. 3. Combinatorial Chemistry By combining molecular “building blocks”, we can create very large numbers of different molecules very quickly. Usually involves a “scaffold” molecule, and sets of compounds which can be reacted with the scaffold to place different structures on “attachment points”.
  • 15. 4. Molecular Modeling • 3D Visualization of interactions between compounds and proteins • “Docking” compounds into proteins computationally
  • 16. 5.3D 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 available
  • 17. “Docking” compounds into proteins computationally
  • 18. 6.In Silico ADME Models Computational methods can predict compound properties important to ADME, e.g. LogP, a liphophilicity measure Solubility Permeability Cytochrome p450 metabolism Means estimates can be made for millions of compouds, helping reduce “atrittion” – the failure rate of compounds in late stage
  • 19. Reference An introduction to cheminformatics:- A.R.Leach Cheminformatics:- Johann Gasteiger and Thomas Engel Molecular modeling-principles and Application:- A.R.Leach

Editor's Notes

  1. Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.
  2. Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.
  3. Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.