C-QSAR database

Hansch, C., Hoekman, D., Leo, A., Weininger, D., Selassie, C.D. C-QSAR Database. Available from the BioByte Corporation, Claremont, CA, 2004, http //WWW. biobyte.com /... 

The first approach mentioned was chosen in the C-QSAR database [34] using Hansch-type QSAR equations [35], Its advantage is that these models are easy to apply and interpret. However, each model has a limited applicability domain (Figure 12.1) thus only toxicity predictions for compounds belonging to these specific classes are suitable. Unfortunately, no analysis of the predictive power of a test set has been published to date. 

Hansch C, Hoekman D, Leo A, Weininger D, Selassie CD, C-QSAR database. Available from the BioByte Corporation, 201 West 4th St. Suite 204, Claremont, CA 91711. 

Kurup A (2003) C-QSAR A database of 18,000 QSARs and associated biological and physical data. J. Comp-Aid. Mol. Des. 17 187-196. 

Contents I. Introduction 34 II. Molecular Descriptors and Physicochemical Properties 36 III. Molecular Databases and Chemical Space 37 IV. Chemoinformatics in Food Chemistry 40 V. Examples of Molecular Similarity, Pharmacophore Modeling, Molecular Docking, and QSAR in Food or Food-Related Components 43 A. Molecular similarity 43 B. Pharmacophore model 47 C. QSAR and QSPR 48 D. Molecular docking 49 VI. Concluding Remarks and Perspectives 52 Acknowledgments 53 References 53... 

C. Hansch, D. Hoekman, and A. Leo, Medchem/Biobyte QSAR Database (1996). Available from Biobyte Corp., 201 W. Fourth St., Suite 204, Claremont, CA 91711. World Wide Web address (URL) http //fox.pomona.claremont.edu/chem/qsar-db. 

Shen M, Beguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds. J Med Chem 2004 47(9) 2356-64. 

Nendza, M. and Russom, C.L., QSAR modeling of the ERL-D fathead minnow acute toxicity database, Xenobiotica, 21, 147-170, 1991. 

Walker, J.D., Waller, C.W., and Kane, S., The endocrine disruption priority setting database (EDPSD) a tool to rapidly sort and prioritize chemicals for endocrine disruption screening and testing, in Handbook on Quantitative Structure Activity Relationships (QSARs) for Predicting Chemical Endocrine Disruption Potentials, Walker, J.D., Ed., SETAC Press, Pensacola, FL, 2003 (in press). 

The next step is to design a set of reactions to synthesize the compounds. One or more reaction databases can be searched to find whether any reactions give the desired structures as products or give structures that are similar to the desired ones. The chemist may also use reaction similarity searching (73)and searching across reaction schemes (e.g., if A + B c + D and C + E F + G a reaction scheme search wiU find the query A — F) (74). Once a reaction is found, the chemist needs to decide what reagents to use in the synthesis and where to obtain them. The selection of reagents will usually be based on a combination of physicochemical property considerations (i.e., QSAR and diversity), tempered by... 

Cruciara G, S Qementi and M Barom 1993 Variable Selection in PLS Analysis. In Kubinyi H (Editor) 3D QSAR in Drug Design Leiden, ESCOM, pp 551-564 Cummins D J, C W Andrews, J A Bentley and M Cory 1996. Molecular Diversity in Chemical Databases Comparison of Medicinal Chemistry Knowledge Bases and Databases of Commercially Available Compounds Journal of Chemical Information and Computer Science 36 750-763. 

Fujita, T. (1995) Quantitative structure-activity analysis and database-aided bioisosteric structural transformation procedure as methodologies of agrochemical design, in Classical and Three-Dimensional QSAR in Agrochemistry, vol. 606 (eds C. Hansch and T. Fujita), American Chemical Society, Washington, DC, pp. 13-34. 

Peny, N.C, Davies, E.K. The Use of 3-D Modelling Databases for Identifying Structure Activity Relationships . In QSAR Quantitative Structure-Activity Relationships in Drug Design, Fauchere, J.L., Ed. Alan R. Liss New York, 1989, pp.189-193. 

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