Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)

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dc.contributor.advisorGuevara Pulido, James Oswaldo
dc.contributor.authorTellez Ruíz, Dania Geraldine
dc.date.accessioned2025-05-21T13:44:26Z
dc.date.available2025-05-21T13:44:26Z
dc.date.issued2025-05
dc.description.abstractEl descubrimiento de fármacos ha sido durante mucho tiempo un proceso costoso y lento, pero el Machine Learning (ML) ha revolucionado esta área al permitir análisis masivos de datos y predicciones precisas de propiedades farmacológicas. Este estudio analiza el impacto del ML en el diseño racional de fármacos desde el año 2000 hasta el 2024, evaluando sus aplicaciones y desafíos. Se realizó una búsqueda sistemática en bases de datos especializadas para identificar publicaciones relevantes en Relaciones Cuantitativas Estructura-Actividad (QSAR) y diseño de fármacos asistido por computadora. Los resultados muestran que el ML ha mejorado la predicción de actividad biológica, toxicidad y farmacocinética, con avances significativos en redes neuronales profundas y modelos generativos. Sin embargo, persisten retos en la interpretabilidad de los modelos, la calidad de los datos y la validación experimental. A pesar de estos desafíos, el ML sigue consolidándose como una herramienta esencial en la química farmacéutica, acelerando el descubrimiento de nuevos fármacos y optimizando el desarrollo de medicamentos. Como estudiante de Química Farmacéutica, vi cómo el ML optimiza la selección de blancos terapéuticos y propiedades ADMET, destacando la importancia de datos de calidad. A pesar de los desafíos, el ML es clave en el desarrollo de medicamentos más seguros y eficaces.
dc.description.abstractenglishDrug discovery has long been a costly and time-consuming process, but Machine Learning (ML) has revolutionized this area by enabling massive data analysis and accurate predictions of pharmacological properties. This study analyzes the impact of ML on rational drug design from 2000 to 2024, assessing its applications and challenges. A systematic search of specialized databases was conducted to identify relevant publications in Quantitative Structure-Activity Relationships (QSAR) and computer-aided drug design. The results show that ML has improved the prediction of biological activity, toxicity and pharmacokinetics, with significant advances in deep neural networks and generative models. However, challenges remain in model interpretability, data quality, and experimental validation. Despite these challenges, ML continues to establish itself as an essential tool in pharmaceutical chemistry, accelerating new drug discovery and optimizing drug development. As a Pharmaceutical Chemistry student, I saw how ML optimizes the selection of therapeutic targets and ADMET properties, highlighting the importance of quality data. Despite the challenges, ML is key in the development of safer and more effective drugs.
dc.description.degreelevelPregradospa
dc.description.degreelevelQuímico Farmacéuticospa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad El Bosquespa
dc.identifier.reponamereponame:Repositorio Institucional Universidad El Bosquespa
dc.identifier.repourlrepourl:https://repositorio.unbosque.edu.co
dc.identifier.urihttps://hdl.handle.net/20.500.12495/14417
dc.language.isoes
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.grantorUniversidad El Bosquespa
dc.publisher.programQuímica Farmacéuticaspa
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightshttps://purl.org/coar/access_right/c_abf2
dc.rights.localAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDiseño racional de farmacos
dc.subjectAprendizaje automático
dc.subjectCADD
dc.subjectRedes neuronales artificiales
dc.subjectImpacto en la química farmacéutica
dc.subject.ddc615.19
dc.subject.keywordsRational drug design
dc.subject.keywordsMachine Learning
dc.subject.keywordsCADD
dc.subject.keywordsArtificial neural networks
dc.subject.keywordsImpact on pharmaceutical chemistry
dc.titleMachine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
dc.title.translatedMachine Learning in rational drug design: new advances in drug discovery (2000-2024)
dc.type.coarhttps://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttps://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis/Trabajo de grado - Monografía - Pregrado

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