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Dr Suman Chakravarti

                                     

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Suman Chakravarti, Ph.D. (Medicinal and Pharmaceutical Chemistry)

Position: Principal Scientist

Phone:    (216) 831-3740

Fax:        (216) 831-3742

E-mails:  chakravarti 'at' multicase.com

               

  

Areas of Expertise:

            Dr Chakravarti has a Ph.D. in Medicinal and Pharmaceutical Chemistry with special emphasis on computer-aided drug design. His expertise includes computational medicinal chemistry, computational toxicology, cheminformatics and drug design. He has substantial research experience in the field of mining chemical structure-activity relationships to be used for the development of better and safer drugs and has an in depth knowledge of US FDA's computational safety assessment of drugs and chemicals.

He has developed novel cheminformatics algorithms and has in depth knowledge and experience of programming languages e.g. C, C++ and C#. Development of better and smarter cheminformatics software is one of his abilities.

Selected Publications:

 ·         Cytotoxicity Mechanisms of Anticancer Biophores, Current Computer Aided Drug Design, 2009, 5, 215-224.

·         Chakravarti, S.K. and Klopman, G., A Structural Analysis of the Differential Cytotoxicity of Chemicals in the NCI-60 Cancer Cell Lines, Bioorganic & Medicinal Chemistry, 2008, 16, 4052-4063.

·         Zhu, H., Sedykh, A., Chakravarti, S.K. and Klopman, G., A new group contribution approach to the calculation of LogP, Current Computer Aided Drug Design, 2005, 2, 563-569.

·         Gilles Klopman, Julian Ivanov, Roustem Saiakhov and Suman Chakravarti, MC4PC-An Artificial Intelligence Approach to the Discovery of Quantitative Structure–Toxic Activity Relationships. In Predictive Toxicology (C. Helma Editor), Chapter 12 (p 423-457), Taylor and Francis Group, FL USA, 2005.

·         Klopman, G., Chakravarti, S.K., Zhu, H., Ivanov, J.M. and Saiakhov, R.D., ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases, Journal of Chemical Information and Computer Science, 2004, 44, 704-715.

·         Klopman, G. and Chakravarti, S.K., Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system, Chemosphere, 2003, 51, 445-449.

·         Klopman, G. and Chakravarti, S.K., Screening of high production volume chemicals for estrogen receptor binding activity (II) by the MultiCASE expert system, Chemosphere, 2003, 51, 461-468.

·         Klopman, G., Chakravarti, S.K., Harris, N, Ivanov, J. and Saiakhov, R.D., In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system, SAR and QSAR in Environmental Research, 2003, 14(2), 165-180.

·         Chakravarti, S.K. and Chaturvedi, S.C., Receptor surface analysis of some antiinflammatory benzimidazoles derivatives, Indian Journal of Pharmaceutical Sciences, 2000, 62(5), 364-370.

·         Chakravarti, S.K. and Chaturvedi, S.C., Molecular shape analysis of some antiinflammatory benzimidazoles, Indian Journal of Pharmaceutical Sciences, 2000, 4, 281-287.

·         Chakravarti, S.K., and Chaturvedi, S.C., A quantitative structure-activity relationship analysis of some substituted oxazolo pyridines and benzimidazoles with antiinflammatory activity, Indian Journal of Pharmaceutical Sciences, 1999, 61(4), 206-212.

·         Chakravarti, S.K., Ajmani, S. and Chaturvedi, S.C., SMRAILS : A lotus 1-2-3 spreadsheet for stepwise multiple parameter regression analysis applied to QSAR analysis, Indian Journal of Pharmaceutical Sciences, 1998, 60(6), 371-374.

·         Chakravarti, S.K., Ajmani, S., Tiwari, M. and Chaturvedi, S.C., A quantitative structure-activity relationship analysis of some Nimesulide analogues using computer-aided molecular modeling, Indian Journal of Pharmaceutical Sciences, 1998, 60(4), 216-221.

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Last modified: 08/01/11

MultiCASE MC4PC-based computational toxicology estimations are intended to be used as part of a weight of evidence approach for hazard and risk assessment that includes laboratory experimental data. Computational toxicology predictions are not intended as a substitute for appropriate animal or clinical studies. Results obtained with certain MC4PC software modules should be regarded as preliminary and subject to change.