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

El 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...

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Autores:
Tellez Ruíz, Dania Geraldine
Tipo de recurso:
https://purl.org/coar/resource_type/c_7a1f
Fecha de publicación:
2025
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
spa
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/14417
Acceso en línea:
https://hdl.handle.net/20.500.12495/14417
Palabra clave:
Diseño racional de farmacos
Aprendizaje automático
CADD
Redes neuronales artificiales
Impacto en la química farmacéutica
615.19
Rational drug design
Machine Learning
CADD
Artificial neural networks
Impact on pharmaceutical chemistry
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Attribution-NonCommercial-NoDerivatives 4.0 International
id UNBOSQUE2_5ff47e68365994e56526861d985a6946
oai_identifier_str oai:repositorio.unbosque.edu.co:20.500.12495/14417
network_acronym_str UNBOSQUE2
network_name_str Repositorio U. El Bosque
repository_id_str
dc.title.none.fl_str_mv Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
dc.title.translated.none.fl_str_mv Machine Learning in rational drug design: new advances in drug discovery (2000-2024)
title Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
spellingShingle Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
Diseño racional de farmacos
Aprendizaje automático
CADD
Redes neuronales artificiales
Impacto en la química farmacéutica
615.19
Rational drug design
Machine Learning
CADD
Artificial neural networks
Impact on pharmaceutical chemistry
title_short Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
title_full Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
title_fullStr Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
title_full_unstemmed Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
title_sort Machine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)
dc.creator.fl_str_mv Tellez Ruíz, Dania Geraldine
dc.contributor.advisor.none.fl_str_mv Guevara Pulido, James Oswaldo
dc.contributor.author.none.fl_str_mv Tellez Ruíz, Dania Geraldine
dc.subject.none.fl_str_mv Diseño racional de farmacos
Aprendizaje automático
CADD
Redes neuronales artificiales
Impacto en la química farmacéutica
topic Diseño racional de farmacos
Aprendizaje automático
CADD
Redes neuronales artificiales
Impacto en la química farmacéutica
615.19
Rational drug design
Machine Learning
CADD
Artificial neural networks
Impact on pharmaceutical chemistry
dc.subject.ddc.none.fl_str_mv 615.19
dc.subject.keywords.none.fl_str_mv Rational drug design
Machine Learning
CADD
Artificial neural networks
Impact on pharmaceutical chemistry
description El 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.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-05-21T13:44:26Z
dc.date.available.none.fl_str_mv 2025-05-21T13:44:26Z
dc.date.issued.none.fl_str_mv 2025-05
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.local.none.fl_str_mv Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.coar.none.fl_str_mv https://purl.org/coar/resource_type/c_7a1f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coarversion.none.fl_str_mv https://purl.org/coar/version/c_ab4af688f83e57aa
format https://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12495/14417
dc.identifier.instname.spa.fl_str_mv Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad El Bosque
dc.identifier.repourl.none.fl_str_mv repourl:https://repositorio.unbosque.edu.co
url https://hdl.handle.net/20.500.12495/14417
identifier_str_mv Universidad El Bosque
reponame:Repositorio Institucional Universidad El Bosque
repourl:https://repositorio.unbosque.edu.co
dc.language.iso.fl_str_mv spa
language spa
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spelling Guevara Pulido, James OswaldoTellez Ruíz, Dania Geraldine2025-05-21T13:44:26Z2025-05-21T13:44:26Z2025-05https://hdl.handle.net/20.500.12495/14417Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coEl 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.PregradoQuímico FarmacéuticoDrug 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.application/pdfAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Diseño racional de farmacosAprendizaje automáticoCADDRedes neuronales artificialesImpacto en la química farmacéutica615.19Rational drug designMachine LearningCADDArtificial neural networksImpact on pharmaceutical chemistryMachine Learning en el diseño racional de fármacos: nuevos avances en el descubrimiento de fármacos (2000-2024)Machine Learning in rational drug design: new advances in drug discovery (2000-2024)Química FarmacéuticaUniversidad El BosqueFacultad de CienciasTesis/Trabajo de grado - Monografía - Pregradohttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aa1. 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