Publicación: QSAR/QSPR as an Application of Artificial Neural Networks
| dc.contributor.author | Montañez Godínez, Narelle | |
| dc.contributor.author | Martínez Olguín, Aracely del Carmen | |
| dc.contributor.author | Deeb, Omar | |
| dc.contributor.author | Garduño-Juárez, Ramón | |
| dc.contributor.author | Ramírez Galicia, Guillermo | |
| dc.contributor.other | Instituto de Química Aplicada | |
| dc.date.accessioned | 2026-01-27T17:36:30Z | |
| dc.date.issued | 2015 | |
| dc.description.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. | |
| dc.identifier.citation | 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. En Artificial Neural Networks (pp. 319–333). Springer New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_19 | |
| dc.identifier.isbn | 978-1-4939-2238-3 | |
| dc.identifier.uri | https://repositorio.unpa.edu.mx/handle/10598/573 | |
| dc.identifier.url | https://doi.org/10.1007/978-1-4939-2239-0_19 | |
| dc.language | Inglés | |
| dc.publisher | Springer New York, NY | |
| dc.relation.ispartof | Artificial Neural Networks, 319-333 | |
| dc.rights | Todos los derechos reservados | |
| dc.rights.holder | Springer | |
| dc.subject | Redes neuronales artificiales | |
| dc.title | QSAR/QSPR as an Application of Artificial Neural Networks | |
| dc.type | Capítulo de libro | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 730016c4-4a78-4c95-b022-25dc6142b369 | |
| relation.isAuthorOfPublication.latestForDiscovery | 730016c4-4a78-4c95-b022-25dc6142b369 | |
| relation.isOrgUnitOfPublication | 62985656-211f-4789-b713-54400b398f21 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | 62985656-211f-4789-b713-54400b398f21 |
Archivos
Bloque original
1 - 1 de 1
Cargando...
- Nombre:
- Abstract_QSAR_QSPR.pdf
- Tamaño:
- 221.4 KB
- Formato:
- Adobe Portable Document Format
Bloque de licencias
1 - 1 de 1
Cargando...
- Nombre:
- license.txt
- Tamaño:
- 4.43 KB
- Formato:
- Item-specific license agreed to upon submission
- Descripción:
