Neural networks: Accurate nonlinear QSAR model for HEPT derivatives
Neural networks: Accurate nonlinear QSAR model for HEPT derivatives
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DOI：
10.1021/ci034047q
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发表时间：
2003-07-01
期刊：
影响因子：
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通讯作者：
Cherqaoui, D
中科院分区：
文献类型：
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作者：
Douali, L;Villemin, D;Cherqaoui, D
研究了一系列作为非核苷类逆转录酶抑制剂（NNRTIs）的1-[2-羟基乙氧基甲基]-6-（苯基硫）胸腺嘧啶]（HEPT）衍生物的非线性定量结构-抗hiv -1活性关系（QSAR）。这项QSAR研究由三层神经网络（NN）进行，使用已知负责抗hiv -1活性的分子描述符。该模型的有效性和分子描述符与抗hiv -1活性之间关系的非线性已被清楚地证明。所得模型在拟合和预测阶段都优于文献中给出的模型。神经网络分析得到的预测活性与实验值非常吻合（r -2 = 0.977，预测r(2) = 0.862）。每个分子特征对抗hiv -1活性变异的影响已经被清楚地阐明。
A nonlinear quantitative structure-anti-HIV-1-activity relationship (QSAR) study was investigated in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine] (HEPT) derivatives acting as nonnucleoside reverse transcriptase inhibitors (NNRTIs). This QSAR study has been undertaken by a three-layered neural network (NN) using molecular descriptors known to be responsible for the anti-HIV-1 activity. The usefulness of the model and the nonlinearity of the relationship between molecular descriptors and anti-HIV-1 activity have been clearly demonstrated. The obtained model outperforms those given in the literature in both the fitting and predictive stages. NN analysis yielded predicted activities in excellent agreement with the experimentally obtained values (R-2 = 0.977, predictive r(2) = 0.862). The effect of each molecular feature on the anti-HIV-1 activity variation has been clearly elucidated.