Quantitative Structure−Activity Relationship Modeling of Dopamine D1Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods

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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.

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