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- Bassan, A
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- Narayanan, D
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Mr. Dilip Narayanan is a chemoinformatics Application Scientist, working on developing tools and technologies for pharmaceutical customers. He has worked for several years on the development of chemoinformatics workflows in ADMET for the InforSense Customer Hub (http://chub.inforsense.com). Dilip is engaged in scientific validation of the OntomineTM platform for ADMET, biological activity prediction and scaffold hopping. His expertise in integrating workflows for drug discovery on customer specific projects, lends a unique perspective on the acceptability of computational tools, especially in Indian Pharmaceutical companies.
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Computational ADMET using ONTOMINETM: A Functional Group and Knowledge-based Approach
Dilip Narayanan (Systems Biology Worldwide)
“Fail early, fail fast”, is a well accepted philosophy in modern automated drug discovery. The earlier a molecule fails in the drug discovery process, the less the chance of costly failures later. Toxicity and ADME issues especially cause many of these late stage failures and also affect the in-vivo therapeutic value of a molecule.
Other than drug discovery, legislation like REACH is making it mandatory for any chemical with significant impact on human lifestyle to be tested for ADMET. Other regulatory guidelines like FDA /OECD guidelines already exist for the same.
With the increasing pressure on reducing animal experiments, Computational toxicology and ADME have an increasingly important role to play. OntomineTM: A platform that builds functional group constraint models for many different kinds of toxicity ,logP and logS, will be used to predict ADMET profiles of drugs. These drugs will be examples that have been withdrawn from the market in the past couple of decades. Toxicity predictions will include Organ related (Hepatotoxicity, nephrotoxicity) Cardiotoxicity (QT interval prolongation, Torsades de Pointes) carcinogenicity, mutagenicity and developmental toxicity or Teratogenicity. The differences and complementarity with other approaches will be discussed.
It will then be used to filter molecules on the basis of solubility, lipophilicity and toxicity to identify desirable molecules in a Non-nucleoside reverse transcriptase inhibitor library.
Users will be able to use the toxicity and ADME prediction modules on their own data.
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