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人工神经网络：作为HIV-1逆转录酶抑制剂的HEPT衍生物的非线性定量构效关系研究。

Artificial neural networks: non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors.

作者信息

Douali Latifa, Villemin Didier, Zyad Abdelmajid, Cherqaoui Driss

机构信息

Département de Chimie, Faculté des Sciences Semlalia BP 2390 Université Cadi Ayyad, Marrakech, Morocco.

出版信息

Mol Divers. 2004;8(1):1-8. doi: 10.1023/b:modi.0000006753.11500.37.

DOI:10.1023/b:modi.0000006753.11500.37
PMID:14964783
Abstract

Structure-anti HIV activity relationships were established for a sample of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine (HEPT) using a three-layer neural network (NN). Eight structural descriptors and physicochemical variables were used to characterize the HEPT derivatives under study. The network's architecture and parameters were optimized in order to obtain good results. All the NN architectures were able to establish a satisfactory relationship between the molecular descriptors and the anti-HIV activity. NN proved to give better results than other models in the literature. NN have been shown to be particularly successful in their ability to identify non-linear relationships.

摘要

利用三层神经网络（NN）建立了80个1-[2-羟基乙氧基甲基]-6-（苯硫基）胸腺嘧啶（HEPT）样品的结构-抗HIV活性关系。使用八个结构描述符和物理化学变量来表征所研究的HEPT衍生物。对网络的架构和参数进行了优化以获得良好的结果。所有的神经网络架构都能够在分子描述符和抗HIV活性之间建立令人满意的关系。与文献中的其他模型相比，神经网络被证明能给出更好的结果。神经网络在识别非线性关系方面的能力已被证明特别成功。

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本文引用的文献

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QSAR for anti-HIV activity of HEPT derivatives.HEPT衍生物抗HIV活性的定量构效关系
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Comparative Quantitative Structureminus signActivity Relationship Studies on Anti-HIV Drugs.抗艾滋病病毒药物的比较定量构效关系研究
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A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.一种使用多元线性回归分析预测抗HIV活性的新型简单定量构效关系模型。
Mol Divers. 2006 Aug;10(3):405-14. doi: 10.1007/s11030-005-9012-2. Epub 2006 Aug 1.
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3D-quantitative structure-activity relationships of HEPT derivatives as HIV-1 reverse transcriptase inhibitors, based on Ab initio calculations.基于从头算的HEPT衍生物作为HIV-1逆转录酶抑制剂的3D定量构效关系
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J Chem Inf Comput Sci. 2000 Jan-Feb;40(1):147-54. doi: 10.1021/ci990314+.
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