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QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

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Abstract

Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a powerful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endocrine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estrogens (training set). Only nine descriptors calculated solely from the molecular structures of compounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The obtained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

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Authors and Affiliations

  1. State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing, 210093, China

    Li Ji, XiaoDong Wang, Si Luo, Liang Qin, XvShu Yang & LianSheng Wang

  2. Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China

    ShuShen Liu

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  1. Li Ji
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  2. XiaoDong Wang
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  3. Si Luo
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  4. Liang Qin
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  5. XvShu Yang
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  6. ShuShen Liu
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  7. LianSheng Wang
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Correspondence to XiaoDong Wang.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 20507008 and 20737001), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK200418) and the National Basic Research Program of China (973 Program)(Grant No. 2003CB415002)

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Ji, L., Wang, X., Luo, S. et al. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network. Sci. China Ser. B-Chem. 51, 677–683 (2008). https://doi.org/10.1007/s11426-008-0070-z

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  • DOI: https://doi.org/10.1007/s11426-008-0070-z

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