Drug design is a very complex process it takes many more times but using the these specific tools we can reduce complex process and save the time and produce a effective new drug that will be helpful in heath environment.
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
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
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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
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
Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.
Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.
Tropsha compares these. Let’s look at some of the comparisons he makes. Mutual similarity techniques.