Assessing the ability of chemical similarity measures to discriminate between active and inactive compounds
- 88 Downloads
A method for assessing the biological discriminating power of chemical similarity measures is presented. The main concern of this work was to develop an objective way of evaluating different similarity measures in terms of how well they distinguished between active and inactive compounds. In addition, we have explored the level of similarity required for optimal separation and commented on its implications for work in the field of chemical diversity studies. The results for one simple similarity measure showed that statistically significant separation could be achieved, and indicated a reasonable similarity value for future work.
KeywordsChemical similarity Biological response Diversity Statistical significance
Unable to display preview. Download preview PDF.
- 1.Brown, R.D., Bures, M.G. and Martin, Y.C.,A comparison of some commercially available structural descriptors and clustering algorithms, In Barach, S.M. (Ed.) Proceedings of the First Electronic Computational Chemistry Conference -CDROM, ARInternet, Landover, MD, 1995.Google Scholar
- 2.Concise Oxford English Dictionary, Oxford University Press, Oxford, U.K., 1981.Google Scholar
- 3.Rouvray, D.H., In Johnson, M.A. and Maggiora, G.M. (Eds.) Concepts and Applications of Molecular Similarity, Wiley, New York, NY, 1990, pp. 15–42.Google Scholar
- 4.Willett, P. and Winterman, V.,A comparison of some measures for the determination of intermolecular structural similarity, Quant. Struct.-Act. Relatsh., 5 (1986) 18–25.Google Scholar
- 5.Daylight Chemical Information Systems, Manual to v434, Santa Fe, NM, 1994.Google Scholar
- 6.Anderberg, M.R., Cluster Analysis for Applications, Academic Press, New York, NY, 1973, p. 89.Google Scholar
- 7.Davies, O.L. and Goldsmith, P.L., Statistical Methods in Research and Production, Oliver and Boyd, Edinburgh, U.K., 1972, p. 78.Google Scholar
- 8.Hall, L.H., Hall Associates Consulting, Professor of Chemistry, Eastern Nazarene College, Quincy, MA.Google Scholar
- 9.Tripos Associates Inc., St. Louis, MO.Google Scholar
- 10.Martin, E.J., Blaney, J.M., Siani, M.A., Spellmeyer, D.C., Wong, A.K. and Moos, W.H.,Measuring diversity: Experimental design of combinatorial libraries for drug discovery, J. Med. Chem., 38 (1995) 1431–1436.Google Scholar
- 11.Taylor, R.,Simulation analysis of experimental design strategies for screening random compounds as potential new drugs and agrochemicals, J. Chem. Inf. Comput. Sci., 35 (1995) 59–67.Google Scholar