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October  9th – 13th, 2007



Programme Conducted by:

AU-KBC Research Centre,

MIT Campus,  Chromepet ,

Chennai -  600 044.





Bioinformatics is an interdisciplinary research area, which may be defined as the application of computational and analytical methods to solve biological problems. This exciting area is a relatively young field, and the pace of research is driven by the large and rapidly increasing amount of data being produced. The data generated by the experimental scientists requires annotation and detailed analysis in order to turn it into knowledge which can then be applied to improve health care via, for example, new drugs and gene therapy, medical practices, and food production - all of which are now high-profile issues.

Bioinformatics - Reducing Drug Discovery and Development Costs / A Holistic Approach to Drug Discovery and Development: 

To develop a new drug conventionally, one has to spend one Billion dollar and 10 –12 years time. There is no guarantee of success in this method, in spite of spending so much time and money. New methods for drug discovery and delivery are receiving considerable attention in the pharmaceutical industry. Trial-and-error discovery methods have been replaced by focused combinatorial chemistry, computer-aided drug design, and other processes. Genomics and Proteomics are complementary technologies of biological research. The potential of these technologies for biomedical revolution will be realized by integrating them into the drug discovery process at every stage from hypothesis generation to clinical evaluation. As biotechnology becomes increasingly important in drug R&D, the application of bioinformatics tools has the potential to drive growth in the worldwide pharmaceuticals drug market from the $240 billion today to $3 trillion by 2020.The forecast value for the worldwide informatics market in the life science sector is between $1.7 and $7 billion by 2007.  The modern drug discovery relies heavily on virtual research facilitated by computing power. Hardware and software companies provide an increasing variety of informatics tools for this effort.

Twentieth century biology has been about cataloging the elements of life. Every day, we have a little more of the recipe of life, stretching before us as an almost endless line of As, Gs, Cs, and Ts--forming general sequences common to most living organisms, gene sequences common to most humans, polymorphisms peculiar to small subpopulations. But this static information amounts to little more than a parts catalog, a shopping list for a living organism. A vital thrust of 21st century biology will be the animation of these static parts. After all, a long string of base pair letters is like a long string of letters. It makes for a less interesting read than a telephone directory, and while it tells you how dial up all sorts of important proteins, most sequences alone tell you little more about a person than does their phone number. We cannot yet predict protein folding from amino acid sequence, nor can we accurately predict protein function from protein shape. We can, of course, correlate certain polymorphisms with likely disease outcomes, and we learn more every day. But the more we learn about the importance of these new variables, the more we have to take into consideration when developing clinical strategies, undertaking drug development, and designing clinical trials. And gene sequence, even when linked to functional information, will only be one of many variables to consider in optimally designing therapeutic interventions and treating disease. 

In recent years, we have seen an explosion in the amount of biological information that is available. Various databases are doubling in size every 15 months and we now have the complete genome sequences of more than 350 organisms including human and another 1000 organisms sequence work is progressing. It appears that the ability to generate vast quantities of data has surpassed the ability to use this data meaningfully. The pharmaceutical industry has embraced genomics as a source of drug targets. It also recognizes that the field of bioinformatics is crucial for validating these potential drug targets and for determining which ones are the most suitable for entering the drug development pipeline. Recently, there has been a change in the way that medicines are being developed due to our increased understanding of molecular biology. In the past, new synthetic organic molecules were tested in animals or in whole organ preparations. This has been replaced with a molecular target approach in which in-vitro screening of compounds against purified, recombinant proteins or genetically modified cell lines are carried out with a high throughput. This change has come about as a consequence of better and ever improving knowledge of the molecular basis of disease.

All marketed drugs today target only about 500 gene products. The elucidation of the human genome, which has an estimated 30,000 to 40,000 genes, presents immense new opportunities for drug discovery and simultaneously creates a potential bottleneck regarding the choice of targets to support the drug discovery pipeline. The major advances in genomics and sequencing means that finding an attractive target is no longer a problem but finding the targets that are most likely to succeed has become the challenge. The focus of bioinformatics in the drug discovery process has therefore shifted from target identification to target validation. 

A lot of factors need to be taken into account concerning a candidate target from a multitude of heterogeneous resources. The types of information that one needs to gather about potential targets include nucleotide and protein sequencing information, homologues, mapping information, function prediction, pathway information, disease associations, variants, structural information, gene and protein expression data and species / taxonomic distribution among others. Different bioinformatics tools can be used to gather this information. The accumulation of this information into databases about potential targets means that the pharmaceutical companies can save themselves much time, effort and expense exerting bench efforts on targets that will ultimately fail. The information that is gathered helps to characterize the different targets into families and subfamilies. It also classifies the behavior of the different molecules in a biochemical and cellular context. Decisions about which families provide the best potential targets are guided by a number of criteria. It is important that the potential target has a suitable structure for interacting with drug molecules. Structural genomics helps to priorities the families in terms of their 3D structures.  

To develop broad spectrum drugs that are effective against a wide range of pathogenic species while at other times we want to develop narrow spectrum drugs that are highly specific to a particular organism. Comparative genomics helps to find protein families that are widely taxonomically dispersed and those that are unique to a particular organism. For example, when we want to develop a broad-spectrum antibiotic, we are looking for targets that are present in a large number of bacteria yet have no similar homologues in human. This means that the antibiotic will be effective against many bacteria killing them while causing no harm to the human. In order to determine the role our potential drug target plays in a particular disease mechanism we use DNA and protein chips. These chips can measure the amount of transcript or protein expressed by a cell at different times or in different states (healthy versus diseased).

Following on from the genomics explosion and the huge increase in the number of potential drug targets, there has been a move from the classical linear approach of drug discovery to a non-linear and high throughput approach. The field of bioinformatics has become a major part of the drug discovery pipeline playing a key role for validating drug targets. By integrating data from many inter-related yet heterogeneous resources, bioinformatics can help in our understanding of complex biological processes and help improve drug discovery.  

These days, computers are an integral part of genomics-based drug discovery, helping researchers find drug targets by comparing databases of genomic information with annotations about functional information, by analyzing the data that comes in from various wet lab experiments, and by simply keeping track of the huge amounts of biological data being unearthed in life sciences research. This is the role of bioinformatics, a field that has exploded in importance over the last few years as companies have begun to realize they are drowning in raw data. But now the uses of computers for other parts of the discovery and development process are coming to the fore. Theoretically, researchers could now test virtual drug compounds against virtual protein targets, study the virtual pharmacokinetics of their optimized virtual lead in what amounts to virtual animals, study its effects on virtual organs, design a virtual clinical trial to test assumptions and variances, and even answer some regulatory questions through simulation. Somewhere in that process, a chemist has to actually mix up a compound and conduct some experiment but buckets of silicon are being added to the discovery and development process every day, with the hope that the wet lab will one day become as dry as a sand box.


To get more information visit the following web sites: 






TOPICS COVERED(tentative):  

*Human Genome Project

*Target Discovery & Validation

*Lead Generation & Optimization

*Drug Discovery and Pharmacogenomics

*RNAi and Drug Discovery

*Genomic, Proteomic and Metabonomic tools in Drug Discovery & Development

*Molecular Modeling


*Structure-Guided Drug Design

*Chemo informatics and Pharmacoinformatics

* Medicinal Chemistry

* Marine environment as treasure trove for Drug Source

* Natural Product Library

*Combinatorial Chemistry

*Antibiotics Resistance and Drug Discovery

*Vaccine development

*HTS for Drug Discovery

*Data Mining for Drug Discovery

*Systems Biology and Drug Discovery

*Synthetic Biology


*Medical Genomics


*Drug Delivery Systems

*Patents, IPR, WTO and Drug Discovery






Interested students can do a project in bioinformatics related area without additional fee and project guidance is available freely for the following areas:

*Drug Discovery

*Structure Based Drug Design

*Micro array Data Analysis

*Functional Genomics, Comparative Genomics and Proteomics

*Comparative metabolomics

*Molecular modeling


*Systems Biology

*Software Development

*Database Development

*Data Mining and Knowledge Discovery






The workshop is targeted towards Graduate and Post–graduate students in Science, Engineering and Technology / Life Science Scientists / Medicine / Pharma, Bioinformatics and Biotechnology Faculty / PhD scholars / PDF’s. The presentation will be in a tutorial style and participant’s interaction will be strongly encouraged.

For each topic, bird’s eye view will be provided in the beginning and a list of tools and databases used also provided. Important tools in each will be demonstrated with few examples. For all categories, exercise will be given to get hands on experience. Course material will be given in the form of CD to each participant.



The course will be conducted in 10 sessions at the AU-KBC Research Centre, Chromepet from   October   9th – 13th, 2007.

Morning Session   - 09:00 AM - 01:00 PM

Afternoon Session - 02:00 PM - 06:00 PM  



Dr.Jaffar Ali (AU KBC)

Dr.G.Ramesh Kumar (AU KBC)

Dr.Suvro Chatterjee (AU KBC)

Dr.K.Krishnamurty (AU KBC)

Dr.CN Ramchand (KEMIN,Chennai)

Ms. T.K.Subazini Vijay (AU KBC)

Mr. K.Palani Kannan (AU KBC)

Mr. S.Nagavignesh (AU KBC)

Mr.K.E.Ravi Kumar (Ph.D) (AU KBC)

Ms.N.Meenakshi(Ph.D) (AU KBC)

Dr.Mukesh Doble (IIT,Chennai)

Dr.Bharath Srinivasan (IIT,Chennai)


Dr.D.Sundar (Bioinformatics Centre, Pondicherry University)

Dr.S.V.Ramanan (Chennai)

Dr.MD.Nair (Chennai)

Mr.Sameer Hassan , (Ph.D) ( TRC,Chennai)



The course fee is Rs. 7,500 /- and for students a concessional rate of Rs. 5,500 /- is available on request. Please send a Cheque / DD drawn in favour of ``The Director, AU-KBC Research Centre'' along with clear contact details (telephone no. and e-mail address). Group of 5 students registering together will be charged Rs.5, 000/-each. 

Last Date for Registration: 08.10.2007. Please note that the number of seats is limited and registration is made on the basis of first come first served. 

Lunch will be provided for participants attending the program.   

Participation certificate will be given at the end of the programme.




Please send your applications with DD to:  




AU-KBC Research Centre,

MIT Campus, Chromepet,

Chennai - 600 044.

Phone: 2223 2711, 2223 4885,2223 6958 and 2223 6959

E-mail: gramesh@au-kbc.org