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Home > MDL® Discovery Predictive Science > MDL® QSAR
 
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MDL QSAR is a comprehensive QSAR modeling system that lets scientists establish reliable quantitative structure-activity and structure-property relationships, create new calculators for in silico screening, and generate new compound libraries based on results–accelerating the discovery of lead compounds in drug and agrochemical research. By providing powerful computational tools in a user-friendly environment, MDL QSAR supports a highly effective drug discovery workflow across all project team disciplines.
Computational Toxicology Applications and MDL-QSAR References

For further details about these papers please contact: Joseph Contrera – jfcontrera@verizon.net, Computational Toxicology Services LLC
  1. Dearden, J. C. In silico prediction of drug toxicity. J. Comput. Aided Mol. Des. 17 (2003) 119-27.

  2. McGee, P. Modeling success with in silico tools. Curr. Opin. Drug Discov. Develop. 8 (2005) 24-28.

  3. Wilson, A.G., White, A.C., Mueller R.A. Role of predictive metabolism and toxicity modeling in drug discovery--a summary of some recent advancements. Curr. Opin. Drug Discov. Devel. 6 (2003) 123-128.

  4. Tunkel, J., Mayo, K., Austin, C., Hickerson, A., Howard P. Practical considerations on the use of predictive models for regulatory purposes. Environ. Sci. Technol. 39 (2005) 2188-2199.

  5. Wagner, P.M., Nabholz, J.V., Kent R.J. The new chemicals process at the Environmental Protection Agency (EPA): structure-activity relationships for hazard identification and risk assessment. Toxicol. Lett. 79 (1995) 67-73.

  6. Bailey, A.B., Chanderbhan, R., Collazo-Braier, N., Cheeseman, M.A., Twaroski, M.L. The use of structure-activity relationship analysis in the food contact notification program. Regul.Toxicol. Pharmacol. 42 (2005) 225-235.

  7. Jacobson-Kram, D. Contrera, J.F. Genetic toxicity assessment: Employing the best science for human safety evaluation part I: Early screening for potential human mutagens. Toxicol. Sci. 96 (2007)16-20.

  8. Kruhlak, N.L. Contrera, J.F. Benz, R. D., Matthews, E. J. Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Advan. in Drug Deliv. 59 (2007) 43-55.

  9. McGovern, T., Jacobson-Kram, D. Regulation of Genotoxic and carcinogenic impurities in drug substances and products. Trends in Anal. Chem. 25 (2006) 790-795.

  10. Contrera, J.F., Matthews, E.J., Benz, R.D. Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. Regul. Toxicol. Pharmacol. 38 (2003) 243-259.

  11. Contrera, J.F., Hall, L.H., Kier, L.B., MacLaughlin, P. QSAR modeling of carcinogenic risk using discriminant analysis and topological molecular descriptors. Curr. Drug Discov. Technol., 2 (2005) 55-57.

  12. Contrera, J.F., Kruhlak, N.L., Matthews, E. J., Benz, R.D. Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. Regul. Toxicol. Pharmacol. 49 (2007) 172-182.

  13. Matthews, E. J., Kruhlak, N.L., Benz, R.D., Contrera, J.F. C.A. Marchant, C. Yang. Combined use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows software to achieve high performance, high confidence, mode of action-based predictions of chemical carcinogenesis in rodents. Toxicology Mech. Methods, In press (2008).

  14. Contrera, J.F., Matthews, E.J., Kruhlak, N.L., Benz R.D. In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL® QSAR software. Regul. Toxicol. Pharmacol. 43 (2005) 313-323.

  15. Contrera, J.F., Matthews, E.J., Kruhlak, N.L., Benz R.D. In silico screening of chemicals for genetic toxicity using MDL-QSAR, non-parametricric discriminant analysis, E-state, connectivity and molecular property descriptors. Toxicology Mech. Methods, 18 (2008) 1-19.

  16. Contrera, J.F., Matthews, E.J., Kruhlak, N.L., Benz R.D. Estimating the safe starting dose in phase I clinical trials and no observed effect level (NOEL) based on QSAR modeling of the human maximum recommended daily dose (MRDD), Regul Toxicol Pharmacol. 40 (2004) 185-206.

  17. Kruhlak, N. L., Choi, S. S., Contrera, J. F., Weaver, J. L.,Willard, J. M., Hastings, K . L., Sancilio L. F. Development of a phospholipidosis database and predictive quantitative structure activity relationship (QSAR) models. Toxicology Mech. Methods, In press (2008).

  18. OECD Joint Meeting on the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. Report on the Regulatory Uses and Applications in the OECD Member Countries of (Quantitative) Structure-Activity Relationship (QSAR) Models in the Assessment of New and Existing Chemicals. (2006) www.oecd.org/ehs/

  19. Pearl, G.M., Livingston-Carr, S., Durham, S.K. Integration of computational analysis as a sentinel tool in toxicological assessments. Curr. Top. Med. Chem. 1 (2001) 247-255.

  20. Zeeman, M., Auer, C. M., Clements, R. G., Nabholz, J. V., and Boethling, R. S. U.S. EPA regulatory perspectives on the use of (Q)SAR for new and existing chemical evaluations. SAR QSAR Environ. Res. (1995) 3:179-201.

  21. Wagner, P. M., Nabholz, J. V., and Kent, R. J. The new chemicals process at the Environmental Protection Agency (EPA): structure-activity relationships for hazard identification and risk assessment. (1995) Toxicol. Lett. 79:67-73.

  22. Drug Information Association. Regulation and Control of Genotoxic Impurities. Bethesda, Maryland, November 17-18, 2005

  23. Valerio, L. G., Arvidson, K. B., Chanderbhan, R. F., Contrera, J. F. Prediction of rodent carcinogenic potential of naturally occurring chemicals in human diet using high-throughput QSAR predictive modeling. Regul. Toxicol. Pharmacol. 222 (2007) 1-16.
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Display a histogram with all relevant structural information

Analyze the contribution of structural descriptors. Here one asks what ranges of E-State values of the OH groups contribute to the pIC50 bioactivities of the benzyl compounds. MDL QSAR displays a histogram and all relevant structural information to provide a quantitative answer.

     
     
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Data analysis tools

Use MDL QSAR's data analysis tools to cluster data, visualize in up to 4 dimensions for principal components, identify outliers, and select/deselect clusters for your model.

Additional Info

QSAR Modeling of Drug Binding to Protein (PDF)

Building a Blood Brain Permeation model with MDL QSAR (PDF)

MDL QSAR datasheet (PDF)

Overview of Blood Brain Permeation model (PDF)

 
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