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 Mailman, Alexander Tropsha

Research output: Contribution to journalArticle

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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

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Quantitative Structure-Activity Relationship
Dopamine Antagonists
Least-Squares Analysis
Structure-Activity Relationship
Ligands

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Drug Discovery

Cite this

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title = "Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms- partial least-squares, and K nearest neighbor methods",
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.",
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Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms- partial least-squares, and K nearest neighbor methods. / Hoffman, Brian; Cho, Sung Jin; Zheng, Weifan; Wyrick, Steven; Nichols, David E.; Mailman, Richard; Tropsha, Alexander.

In: Journal of Medicinal Chemistry, Vol. 42, No. 17, 26.08.1999, p. 3217-3226.

Research output: Contribution to journalArticle

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AU - Nichols, David E.

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AU - Tropsha, Alexander

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