symposium THEme

                Human Genome Project is largely complete, so the problem facing by scientists is how to maximize the use of the newly acquired data for improving health care. It is estimated that the number of therapeutic targets available for drug discovery will increase from the current number of 600 to 1000 to perhaps as many as 5000 to 10,000. In addition to the challenges that this number provides to the pharmaceutical industry, there is the issue of sequence variation through Single Nucleotide Polymorphisms (SNPs), some of which may impact upon the way that the body handles drug treatment. This may be due to a direct effect on the binding site of the protein target through non-conservative alterations of the amino acid sequence, or else through indirect effects on drug metabolizing enzymes. In order to meet these challenges, the combination of structural proteomics and computer-aided small molecule design provides opportunities for creating new molecules in silico; these may be designed to bind to selected pharmacogenetic variants of a protein in order to overcome the non-responsiveness of certain patient groups to a particular medicine.
                
In silico drug discovery methods reduce both time and cost of development of novel drug/lead molecules. Knowledge about the molecule, its interaction with the drug, molecular modeling, and new drug development creates an awareness of the molecule at the organ level and aids in the prevention, diagnosis, and treatment of diseases. Computational tools have the advantage of delivering new lead candidates more quickly and at lower cost. In the 21st century, in silico methods facilitate target identification, structure prediction and lead/drug discovery. The computational methods expect more reliable and expeditious protocols for development of novel ideas, which increase the potential leads. The computer-assisted molecular design has succeeded in the QSAR and protein modeling algorithms to improve activity of lead compounds. Selecting candidates via the described in silico virtual screening, should help to reduce the list of candidate molecules and thereby reduce time and costs significantly.
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Plenary Speakers
  • Dr. A. Anand
    VIT University, Vellore
  • Dr. Arpita Yadav
    C.S.J.M. University, Kanpur
  • Dr. N. Gautham
    University of Madras, Chennai
  • Dr. P. Gautam
    Anna University, Chennai
  • Dr. P. Karthe
    University of Madras, Chennai
  • Dr. N. Manoj
    Indian Institute of Technology Madras, Chennai
  • Dr. Mukesh Doble
    Indian Institute of Technology Madras, Chennai
  • Dr. Punit Kaur
    All India Institute of Medical Sciences, New Delhi
  • Dr. K. Sekar
    Indian Institute of Science, Bangalore
  • Dr. S. Selvaraj
    Bharathidasan University, Tiruchirappalli
  • Dr. K. Sivakumar
    Anna University, Chennai
  • Dr. V. Subramanian
    Central Leather Research Institute, Chennai
  • Dr. D. Sundar
    Indian Institute of Technology, Delhi
  • Dr. B. Syed Ibrahim
    Pondicherry University, Puducherry
  • Dr. B. K. Tiwary
    Pondicherry University, Pudhucherry
  • Dr. D. Velmurugan
    University of Madras, Chennai
  • Mr. R. Raghu
    Schrodinger GmbH, USA


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