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Artificial Neural Networks pp 319–333Cite as

QSAR/QSPR as an Application of Artificial Neural Networks

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1260))

Abstract

Quantitative Structure–Activity Relationships (QSARs) and Quantitative Structure–Property Relationships (QSPRs) are mathematical models used to describe and predict a particular activity/property of compounds. On the other hand, the Artificial Neural Network (ANN) is a tool that emulates the human brain to solve very complex problems. The exponential need for new compounds in the drug industry requires alternatives for experimental methods to decrease development time and costs. This is where chemical computational methods have a great relevance, especially QSAR/QSPR-ANN. This chapter shows the importance of QSAR/QSPR-ANN and provides examples of its use.

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Acknowledgement

AC Martínez-Olguín wishes to thank CONACyT for a graduate scholarship. The English was kindly reviewed by Miss Désirée Argott.

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Authors and Affiliations

  1. División de Estudios de Posgrado, Universidad del Papaloapan, Tuxtepec, Oaxaca, México

    Narelle Montañez-Godínez, Aracely C. Martínez-Olguín & Guillermo Ramírez-Galicia

  2. Faculty of Pharmacy, Al-Quds University, 20002, Jerusalem, Palestine

    Omar Deeb

  3. Instituto de Ciencias Fisicas, Universidad Nacional Autonoma de Mexico, Cuernavaca, Mexico

    Ramón Garduño-Juárez

Authors
  1. Narelle Montañez-Godínez
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  2. Aracely C. Martínez-Olguín
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  3. Omar Deeb
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  4. Ramón Garduño-Juárez
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  5. Guillermo Ramírez-Galicia
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Corresponding author

Correspondence to Omar Deeb .

Editor information

Editors and Affiliations

  1. Chemistry Department, Oxford University, UK, and University of Victoria, Victoria, British Columbia, Canada

    Hugh Cartwright

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Montañez-Godínez, N., Martínez-Olguín, A.C., Deeb, O., Garduño-Juárez, R., Ramírez-Galicia, G. (2015). QSAR/QSPR as an Application of Artificial Neural Networks. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_19

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  • DOI: https://doi.org/10.1007/978-1-4939-2239-0_19

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2238-3

  • Online ISBN: 978-1-4939-2239-0

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