Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods
Quantitative structure-activity relationship modeling of dopamine D1 antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods
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DOI：
10.1021/jm980415j
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发表时间：
1999-08-26
影响因子：
7.3
通讯作者：
Tropsha, A
中科院分区：
文献类型：
--
作者：
Hoffman, B;Cho, SJ;Tropsha, A
应用多种定量构效关系(QSAR)方法对29种不同化学结构的D-1多巴胺拮抗剂进行了研究。除了传统的3D比较分子场分析(CoMFA)外，我们还使用了交叉验证的R-2引导区域选择(Q(2)-GRS)CoMFA(见参考文献1)，以及我们实验室最近开发的两种新的变量选择QSAR方法。这些方法包括遗传算法-偏最小二乘法(GA-PLS)和K近邻(KNN)法(见参考文献2-4)，它们利用了化学结构的二维拓扑描述符。每种QSAR方法都产生一个高度预测的模型，交叉验证的R-2(Q(2))值CoMFA为0.57，Q(2)-GRS为0.54，GA-PLS为0.73，KNN为0.79。所有QSAR方法的成功都表明这类化合物存在内在的构效关系，并为设计和预测新的D1配体的生物活性提供了更可靠的设计和预测。
Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D-1 dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R-2 guided region selection (q(2)-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 R-2 (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-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.