Computer-Aided Drug Designing (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions CADD methods are heavily dependent on bioinformatics tools, applications, and databases

1. Computer-Aided Drug
Designing (CADD)
Siddharth Singh
1314354041

2. Classification

3. Computer-Aided Drug Designing (CADD)
oComputer-Aided Drug Designing (CADD) is a
specialized discipline that uses computational
methods to simulate drug-receptor interactions
oCADD methods are heavily dependent on
bioinformatics tools, applications and databases

4. R&D spending up, new drugs down

5. .
 .
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 &
Scale-up
Human clinical trials
(2-10 years)
FDA approval
(2-3 years)

6. Bioinformatics
Supports
CADD
Research
Virtual High-Throughput
Screening (vHTS)
Sequence Analysis
Homology Modeling
Similarity Searches
Drug Lead Optimization
Physicochemical Modeling
Drug Bioavailability and
Bioactivity

7. Virtual High-Throughput
Screening (vHTS)
oThe protein targets are screened against databases of small-
molecule compounds
oWith today’s computational resources, several million
compounds can be screened in a few days on sufficiently
large clustered computers
oThis method provides a handful of promising leads
e.g. ZINC is a good example of a vHTS compound library

8. Sequence Analysis
oIt is very useful to determine how similar or dissimilar the
organisms are based on gene or protein sequences
oWith this information one can infer the evolutionary
relationships of the organisms
oThere are many bioinformatic sequence analysis tools that
can be used to determine the level of sequence similarity
e.g. DNA sequence analysis, gel electrophoresis

9. Homology Modeling
oA common challenge in CADD research is determining the
3-D structure of proteins
oThe 3-D structure for only a small fraction of the proteins is
known
oBioinformatics software tools are then used to predict the 3-D
structure of the target based on the known 3-D structures of
the templates
oE.g. MODELLER
SWISS-MODEL Repository

10. Similarity Searches
o A common activity in biopharmaceutical companies is the
search for drug analogues
o Starting with a promising drug molecule, one can search for
chemical compounds with similar structure or properties to a
known compound
o A variety of bioinformatics tools and search engines are
available for this work

11. Benefits of CADD
oVirtual screening, lead optimization and predictions of
bioavailability and bioactivity can help guide experimental
research
oOnly the most promising experimental lines of inquiry can be
followed and experimental dead-ends can be avoided early
based on the results of CADD simulations

12. Benefits of CADD
Time-to-Market:
oCADD has predictive power
oIt focuses drug research on specific lead candidates and
avoids potential “dead-end” compounds

13. .
CADD and bioinformatics together are
a powerful combination in drug
research and development.

14. Research Achievements
oSoftware developed
oBioinformatics database developed

15. Softwares developed
oSVMProt: Protein function prediction software
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
oINVDOCK: Drug target prediction software
oMoViES: Molecular vibrations evaluation server
http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl

16. Bioinformatics databases developed
oTherapeutic target database
http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp
o Drug adverse reaction target database
http://xin.cz3.nus.edu.sg/group/drt/dart.asp
o Drug ADME associated protein database
http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp
o Kinetic data of bio molecular interactions
database
http://xin.cz3.nus.edu.sg/group/kdbi.asp
oComputed ligand binding energy database
http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp