title: QSAR/QSPR as an application of artificial neural networks.
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.
authors.affiliation: División de Estudios de Posgrado, Universidad del Papaloapan, Tuxtepec, Oaxaca, México.
authors.forename: Narelle
authors.initials: N
authors.lastname: Montañez-Godínez
authors.forename: Aracely C
authors.initials: AC
authors.lastname: Martínez-Olguín
authors.forename: Omar
authors.initials: O
authors.lastname: Deeb
authors.forename: Ramón
authors.initials: R
authors.lastname: Garduño-Juárez
authors.forename: Guillermo
authors.initials: G
authors.lastname: Ramírez-Galicia
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008956:Models, Chemical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012984:Software