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Una nueva perspectiva sobre el uso de la inteligencia artificial y la nanoinformática para el desarrollo de
Nandita Tyagi1, Sneha Singh1, Seema Dagar2
1Guru Jambheshwar University of Science and Technology, a University in Hisar, Haryana.
Current topics in medicinal chemistry
|January 22, 2026
Resumen
La inteligencia artificial (IA) y el aprendizaje automático (ML) aceleran el desarrollo de nanomedicamentos al optimizar las propiedades fisicoquímicas. La nanoinformática y los modelos farmacocinéticos basados en fisiología (PBPK) mejoran el diseño de fármacos, predicen la distribución y evalúan la toxicidad para terapias más seguras y eficaces.
Área de la Ciencia:
- Ingeniería Biomédica
- Química Computacional
- Descubrimiento de Fármacos
Sus antecedentes:
- El desarrollo de fármacos es costoso y requiere mucho tiempo.
- La inteligencia artificial (IA) y el aprendizaje automático (ML) ofrecen soluciones para el diseño de nanomedicamentos traslacionales.
- La eficacia de los nanomedicamentos depende de propiedades fisicoquímicas como el tamaño, la forma y la carga.
Objetivo del estudio:
- Revisar la implementación de la nanoinformática y la IA en la traslación de nanomedicamentos.
- Destacar las estrategias computacionales para el diseño de sistemas de administración de nanopartículas.
- Examinar las contribuciones, los desafíos y las direcciones futuras de la IA/ML en el desarrollo de nanomedicamentos.
Principales métodos:
- Modelos de relación cuantitativa estructura-actividad/propiedad (QSAR/QSPR) para la optimización de propiedades.
- Modelos farmacocinéticos basados en fisiología (PBPK) para predecir la biodistribución y la toxicidad.
- Herramientas de IA/ML para el diseño de sistemas de administración de nanopartículas, evaluación de nanotoxicidad y desarrollo de modelos de simulación.
Principales resultados:
- La nanoinformática permite la optimización sistemática de las propiedades de los nanomedicamentos para mejorar su funcionalidad.
- Los modelos PBPK predicen la distribución de fármacos y nanomedicamentos, lo que ayuda en la evaluación de la toxicidad.
- La IA/ML facilita la selección de nanomateriales y el desarrollo de modelos de simulación in vitro/in vivo.
Conclusiones:
- La IA y la nanoinformática aceleran significativamente la traslación de nanomedicamentos desde el laboratorio hasta la clínica.
- Los enfoques computacionales minimizan los riesgos para la salud y el medio ambiente durante el desarrollo de nanomedicamentos.
- Las metodologías discutidas tienen una amplia aplicabilidad en múltiples disciplinas científicas, incluido el descubrimiento de fármacos y la nanotecnología.
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