Computer aided drug designing (CADD)

CADD is a mixture of bioinformatics and computer science where the information from bioinformatics is combined into a software which makes it easier to process.

1. Computer-Aided Drug Designing (CADD) Aakshay Subramaniam Aniketh Rao

2. Bioinformatics An application of C omputer Science to biological and Drug Development science Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline The ultimate goal of the field is to enable the discovery of new biological insights

3. Classification Information Technology Molecular Biology Genomics & proteomics CADD Computational Resources Applications/Databases

4. Computer-Aided Drug Designing (CADD) 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

5. R&D spending up, new drugs down

6. 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) File IND File NDA

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

8. Sequence Analysis It is very useful to determine how similar or dissimilar the organisms are based on gene or protein sequences With this information one can infer the evolutionary relationships of the organisms There 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 A common challenge in CADD research is determining the 3-D structure of proteins The 3-D structure for only a small fraction of the proteins is known Bioinformatics software tools are then used to predict the 3-D structure of the target based on the known 3-D structures of the templates E.g. MODELLER SWISS-MODEL Repository

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

11. Benefits of CADD The Tufts Report suggests that the cost of drug discovery and development has reached $800 million for each drug successfully brought to market Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden

12. Benefits of CADD Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research Only 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

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

14. Benefits of CADD Insight: CADD provides a deep insight to the drug-receptor interactions acquired by the researchers Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way

15. The Thalidomide Tragedy Structure of Thalidomide THALIDOMIDE

16. Structure of Penicillin

17. Penicillin G Penicillin V Nafcillin Methicillin

18. 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

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

20. Research Achievements Software developed Bioinformatics database developed

21. Softwares developed SVMProt : Protein function prediction software http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi INVDOCK: Drug target prediction software MoViES : Molecular vibrations evaluation server http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl

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