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With the advent of High Performance Computing and Life Science Algorithms,
computational tools are playing major role in new drug discovery. LeadInvent
with its proprietory technologies: SanjeeviniPro & BhageerathPro is well positioned
to harness these advancements.
Leveraging compute power of SCFBio, IIT Delhi′s Supercomputer dedicated to computational biology with
over five hundred billion operation per second compute capacity, LeadInvent provides a distinct advantage
of high performance biocomputing.
In fact, BhageerathPro has been demonstrated to scale almost
linearly on 1024 processors of BlueGene architecture.
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There are multiple stages in drug design where innovative ideas and solutions can
improve efficiency and accuracy of R&D. Broadly speaking, drug design exercise could be divided
into Preclinical stage, Clinical testing stage and Post market stage. Preclinical stage is the
discovery stage of drug design. This is the first step that involves early design and testing before
promising molecules are put through human testing. The success of clinical trials (the costliest stage)
depends upon accuracy of prediction at preclinical stage. The idea here is to fail fast, fail early.
Preclinical hurdles:
- Time spent at this stage is around 3-5 years.
- Large portion of the funding is required to come up with therapeutic molecule.
Preclinical opportunity:
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The primary challenge in lead molecule design requires careful selection of protein or nuclear receptor involved in disease expression. This challenge requires substantial experimental work to obtain biological knowledge about the disease target, its function and its three dimensional structure. In some of the cases, it becomes difficult to obtain structure of target protein through experimental techniques.Thus, rational drug design cannot be undertaken for such cases.
LeadInvent proteomics capabilities provides for a computer simulation based protein structure prediction methodology. This methodology implements rigorous parallel processing simulation algorithms to model and predict the three dimensional structure of a protein from its amino acid sequence. Starting with the amino acid sequence, this methodology generates large number of possible conformations resulting in several folded protein structures. Good structures are filtered out from bad ones using biophysical filters, which is followed by structural refinement & shape optimization through various simulation techniques such as energy minimization & Monte Carlo flexible simulations. Finally a highly customizable energy function is used to score and select best possible structures
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