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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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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