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Computational Approaches to Anti-cancer Drug Design Sarah Taylor ’03, Steven Feldgus and George C. Shields The development of non-steroidal drugs in the treatment of estrogen receptor positive breast cancers aims to produce selective estrogen receptor modulators (SERMs) that would decrease the risk of breast and uterine cancer but mimic beneficial estrogen activity in other areas, such as the cardiovascular system. We are using the 3D-QSAR program Catalyst1 to develop structure-activity relationships based on FDA-approved drugs, such as tamoxifen and raloxifene, as well as other tested inhibitors. From a training set of molecules, Catalyst generates a pharmacophore model, also known as a hypothesis, that attempts to explain the biological activity of the ligands through a threedimensional arrangement of functional groups, such as hydrogen-bond acceptors or hydrophobic groups. In this study, we have used a training set of raloxifene analogs to generate various hypotheses that will provide information necessary to find or synthesize new anti-breast-cancer drugs2. NMe 2 O O O N OH HO Tamoxifen S Raloxifene Research supported by an NIH grant to George Shields. 1 Accelrys, Inc., Catalyst v.4.7. San Diego: Accelrys Inc., 2002. Grese, T. A., Cho, S., Finley, D. R., Godfrey, A. G., Jones, C. D., Lugar III, C. W., Martin, M. J., Matsumoto, K., Pennington, L. D., Winter, M. A., Adrian, D., Cole, H. W., Magee, D. E., Phillips, D. L., Rowley, E. R., Short, L. L., Glasebrook, A. L, and H. U. Bryant. J. Med. Chem. 1997, 40, 146-167. 2