Publicación:
QSAR/QSPR as an Application of Artificial Neural Networks

dc.contributor.authorMontañez Godínez, Narelle
dc.contributor.authorMartínez Olguín, Aracely del Carmen
dc.contributor.authorDeeb, Omar
dc.contributor.authorGarduño-Juárez, Ramón
dc.contributor.authorRamírez Galicia, Guillermo
dc.contributor.otherInstituto de Química Aplicada
dc.date.accessioned2026-01-27T17:36:30Z
dc.date.issued2015
dc.description.abstractQuantitative 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.
dc.identifier.citationMontañez-Godínez, N., Martínez-Olguín, A. C., Deeb, O., Garduño-Juárez, R., & Ramírez-Galicia, G. (2015). QSAR/QSPR as an application of artificial neural networks. En Artificial Neural Networks (pp. 319–333). Springer New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_19
dc.identifier.isbn978-1-4939-2238-3
dc.identifier.urihttps://repositorio.unpa.edu.mx/handle/10598/573
dc.identifier.urlhttps://doi.org/10.1007/978-1-4939-2239-0_19
dc.languageInglés
dc.publisherSpringer New York, NY
dc.relation.ispartofArtificial Neural Networks, 319-333
dc.rightsTodos los derechos reservados
dc.rights.holderSpringer
dc.subjectRedes neuronales artificiales
dc.titleQSAR/QSPR as an Application of Artificial Neural Networks
dc.typeCapítulo de libro
dspace.entity.typePublication
relation.isAuthorOfPublication730016c4-4a78-4c95-b022-25dc6142b369
relation.isAuthorOfPublication.latestForDiscovery730016c4-4a78-4c95-b022-25dc6142b369
relation.isOrgUnitOfPublication62985656-211f-4789-b713-54400b398f21
relation.isOrgUnitOfPublication.latestForDiscovery62985656-211f-4789-b713-54400b398f21

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