title: Binary quantitative structure-activity relationship (QSAR) analysis of estrogen receptor ligands.
abstract: 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 the 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.
authors: Computational Chemistry and Informatics, MDS Panlabs, Bothell, Washington 98011, USA. hgao@panlabs.com
authors: H
authors: H
authors: Gao
authors: C
authors: C
authors: Williams
authors: P
authors: P
authors: Labute
authors: J
authors: J
authors: Bajorath
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical; D045166:Estradiol Congeners / Q000737:chemistry* / Q000378:metabolism / Q000494:pharmacology*; D008024:Ligands; D008956:Models, Chemical; D011960:Receptors, Estrogen / Q000187:drug effects* / Q000378:metabolism*; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
chemical_list: D045166:Estradiol Congeners; D008024:Ligands; D011960:Receptors, Estrogen