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dc.contributor.authorCanal-Alonso, Ángel
dc.contributor.authorJiménez, Pedro
dc.contributor.authorEgido, Noelia
dc.contributor.authorPrieto Tejedor, Javier 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2023-10-04T11:23:40Z
dc.date.available2023-10-04T11:23:40Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10366/153153
dc.description.abstract[EN]Artificial intelligence (AI) has emerged as a transformative tool in the pharmaceutical industry, revolutionizing the traditional drug discovery and development process. Through advanced generative techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), the exploration and design of novel and viable therapeutic molecules has been enhanced. Additionally, AI facilitates the optimization of these molecules by guaranteeing desirable properties and accelerates the identification of therapeutic targets through deep analysis of biomedical and genomic data sets. One of the most significant advances has been drug repurposing, where AI unlocks the hidden potential of known drugs for new therapeutic indications..es_ES
dc.language.isoenges_ES
dc.subjectGenerative Artificial Intelligencees_ES
dc.subjectDrug Designes_ES
dc.subjectComputational Modelses_ES
dc.subject.meshDrug Design *
dc.titleRevolutionizing Pharmaceuticals: applications and potential of Generative Artificial Intelligence in drug discoveryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.subject.unesco2390.01 Diseño. Síntesis y Estudio Nuevos Fármacoses_ES
dc.relation.projectIDCCTT3/20/SA/0003es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.decsdiseño de fármacos *


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