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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
Research supported by an NIH grant to George Shields.
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.