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dc.contributor.authorCanal-Alonso, Ángel
dc.contributor.authorEgido, Noelia
dc.contributor.authorJiménez, Pedro
dc.contributor.authorPrieto Tejedor, Javier 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2023-10-03T08:29:26Z
dc.date.available2023-10-03T08:29:26Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10366/153118
dc.description.abstract[EN]In the modern era of medicine, artificial intelligence (AI) is playing an increasingly prominent role in pharmacology. This article highlights the transformative influence of generative AI in the study and optimization of drug-protein interactions. Through the detailed analysis of models such as Generative Adversarial Networks (GANs) and Transformers, it is illustrated how AI can predict and refine interactions between drugs and proteins, leading to more efficient and safer therapies. Additionally, the ability of generative AI to predict cross drug interactions is discussed, thereby reducing the potential for side effects and complications related to polypharmacy. The conclusion highlights the synergy between traditional pharmacological research and AI,es_ES
dc.description.sponsorshipThis study has been funded by the AIR Genomics project (with file number CCTT3/20/SA/0003), through the call 2020 R&D PROJECTS ORIENTED TO THE EXCELLENCE AND COMPETITIVE IMPROVEMENT OF THE CCTT by the Institute of Business Competitiveness of Castilla y León and FEDER fundses_ES
dc.language.isoenges_ES
dc.subjectGenerative Artificial Intelligencees_ES
dc.subjectDrug Designes_ES
dc.subjectMolecular interactiones_ES
dc.titleGenerative Artificial Intelligence in Pharmacology: Revolutionizing Drug-Protein Interactiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.subject.unesco2390 Química Farmacéuticaes_ES
dc.relation.projectIDCCTT3/20/SA/0003es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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