Douali Latifa, Villemin Didier, Cherqaoui Driss
Département de Chimie, Faculté des Sciences Semlalia BP 2390 Université Cadi Ayyad, Marrakech, Morocco.
J Chem Inf Comput Sci. 2003 Jul-Aug;43(4):1200-7. doi: 10.1021/ci034047q.
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.
对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-(苯硫基)胸腺嘧啶（HEPT）衍生物进行了非线性定量构效关系（QSAR）研究。该QSAR研究由三层神经网络（NN）采用已知与抗HIV-1活性相关的分子描述符进行。该模型的实用性以及分子描述符与抗HIV-1活性之间关系的非线性已得到明确证明。所获得的模型在拟合和预测阶段均优于文献中给出的模型。神经网络分析得出的预测活性与实验获得的值高度吻合（R(2) = 0.977，预测r(2) = 0.862）。每个分子特征对抗HIV-1活性变化的影响已得到清晰阐明。
