Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms- partial least-squares, and K nearest neighbor methods

Brian Hoffman, Sung Jin Cho, Weifan Zheng, Steven Wyrick, David E. Nichols, Richard B. Mailman, Alexander Tropsha

Research output: Contribution to journalArticle

93 Scopus citations

Abstract

Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D1 dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R2 guided region selection (q2-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R2 (q2) values of 0.57 for CoMFA, 0.54 for q2-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D1 ligands.

Original languageEnglish (US)
Pages (from-to)3217-3226
Number of pages10
JournalJournal of Medicinal Chemistry
Volume42
Issue number17
DOIs
StatePublished - Aug 26 1999

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Drug Discovery

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