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Binary Quantitative Structure−Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands

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Computational Chemistry and Informatics, MDS Panlabs, 11804 North Creek Parkway South, Bothell, Washington 98011, Chemical Computing Group Inc., 1255 University Street, Suite 1600, Montreal, Quebec, Canada H3B 3X3, and Department of Biological Structure, University of Washington, Seattle, Washington 98195
Cite this: J. Chem. Inf. Comput. Sci. 1999, 39, 1, 164–168
Publication Date (Web):December 16, 1998
Copyright © 1999 American Chemical Society

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    The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure−activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., “active” versus “inactive”), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.

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     This manuscript is dedicated to Professor Corwin Hansch on the occasion of his 80th birthday.


     Corresponding author. Telephone:  (425)487-8200 ext. 390. Fax:  (425)487-3787. E-mail:  [email protected].

     Computational Chemistry and Informatics.


     Chemical Computing Group Inc.

     Department of Biological Structure.

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