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Repositioning Of Different Chemical Classes For Anti TB Virtual Screening

Repositioning Of Different Chemical Classes For Anti TB Virtual Screening. Project Review By Ayisha Safeeda TCOF 3.5. Pesticide Repositioning For TB Drug Discovery. Aim

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Repositioning Of Different Chemical Classes For Anti TB Virtual Screening

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  1. Repositioning Of Different Chemical Classes For Anti TB Virtual Screening Project Review By AyishaSafeeda TCOF 3.5

  2. Pesticide Repositioning For TB Drug Discovery Aim To create a database of pesticide molecules showing anti tubercular activity and do virtual screening to find the lead compounds.

  3. Data Collection Around thousand pesticides effective against mycobacterium tuberculosis have been collected from various literatures; • Journal of Sciences, Islamic Republic of Iran Synthesis of some New Thiosemicarbazide and 1,3,4-Thiadiazole Heterocycles Bearing Benzo[b]Thiophene Nucleus as a Potent Antitubercular and Antimicrobial Agents S.L. Vasoya, D.J. Paghdar, P.T. Chovatia, and H.S. Joshi* • Understanding Tuberculosis - New Approaches to Fighting Against Drug Resistance Cinnamic Derivatives in Tuberculosis Prithwiraj De1,2, Damien Veau1,2, Florence Bedos-Belval1,2, Stefan Chassaing1,2 and Michel Baltas1,2

  4. RASAYAN J.Chem. Vol4 • Toxicity risk assessment of some novel quinoxalines • A.Puratchikody1, Mukesh Doble2 and N.Ramalakshmi3, • International Journal of Pharmaceutical Sciences and Drug Research 2010; • Pyrazoline Derivatives: A Worthy Insight into the Recent Advances and • potential pharmacological activities • Md. Azizur Rahman1*, Anees A. Siddiqui2 • Etc.,

  5. and databases like • ChEBI- The database and ontology of Chemical Entities of Biological Interest • pubchem • EPA, environmental protection agency search engine • Are also resourceful. • The SDF file of around 200 pesticide structures are created by drawing using the tool Marvin sketch. around 400 molecules are downloaded from pubchem and chEBI.

  6. Model generation • AID 1332 Pubchem bioassay dataset aid 1332 is used for model generation • Power MV The 179 descriptors are generated using the tool power MV • Waikato environment for knowledge analysis Weka is a collection of machine learning algorithms for data mining task. Random forest classifier is used to build the model but higher FP rate and low accuracy leads to the use of cost sensitive classifier.

  7. Not the end The work, tuning and deriving the best model will continue… The screened active molecules will go onto the clustering process And selected molecules will further go for clinical trials

  8. Building a model aid1332 Screening process Using pesticides Target based drug discovery Clustering process Active molecules are clustered Synthesis of selected molecules & clinical trials

  9. Acknowledgement • CSIR-OSDD research unit • Dr. U C A Jaleel • Prof Dr Samir K Brahmachari • Dr BheemaraoUgarkar • All TCOF 3 members • family

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