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Un enfoque interdisciplinario en la enseñanza de la predicción de la estructura secundaria de ARN a los estudiantes
Kamanasish Bhattacharjee1, Adi Idris2,3
1Department of Analytics, School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Journal of microbiology & biology education
|August 22, 2025
Resumen
La inteligencia artificial (IA) y el aprendizaje automático (ML) están revolucionando el diseño terapéutico de ARN. Este estudio presenta un taller basado en la investigación que utiliza herramientas de IA para enseñar a los estudiantes universitarios sobre el diseño y la predicción de la estructura del ARN de pequeña interferencia (siRNA).
Área de la Ciencia:
- Biotecnología y bioinformática
- Biología computacional
- Terapia con ARN
Sus antecedentes:
- La inteligencia artificial (IA), el aprendizaje automático (ML) y el aprendizaje profundo (DL) se integran cada vez más en el diseño terapéutico de biología y ARN.
- Las terapias de ARN pequeño interferente (siRNA) ofrecen potencial para enfermedades genéticas, pero el diseño sigue siendo un desafío, que requiere la predicción de la eficacia y las estructuras secundarias.
- Hay una brecha en la integración de herramientas de IA en los currículos de pregrado para la enseñanza del diseño de siRNA.
Objetivo del estudio:
- Desarrollar un taller basado en la investigación y no en laboratorios para estudiantes de primer año de ciencias de la salud.
- Educar a los estudiantes sobre la utilización de los conceptos de ciencia de la IA y el ARN para el diseño y la predicción de la estructura del siRNA.
- Para cerrar la desconexión entre los avances de la IA y el diseño de siRNA en las prácticas educativas actuales.
Principales métodos:
- Un taller basado en la investigación fue diseñado para estudiantes universitarios.
- Los estudiantes exploraron una herramienta de predicción de la estructura 3D de ARN basada en el aprendizaje profundo automatizado (DL) (trRosettaRNA).
- El taller se centró en determinar las estructuras secundarias de los siRNA y la integración de la IA con la ciencia del ARN.
Principales resultados:
- El taller integró con éxito los conceptos de la ciencia de la IA y el ARN en un formato simplificado.
- Los estudiantes se involucraron con una herramienta DL automatizada para la predicción de la estructura del ARN.
- La actividad proporcionó un enfoque práctico y no de laboratorio para comprender los desafíos de diseño del siRNA.
Conclusiones:
- Las herramientas de IA y ML simplifican significativamente el diseño terapéutico de ARN, incluido el desarrollo de siRNA.
- Las iniciativas educativas son cruciales para equipar a los futuros científicos con habilidades de IA en el diseño de medicamentos.
- Este taller ofrece un modelo para la educación interdisciplinaria en terapias de ARN impulsadas por IA para estudiantes de pregrado en ciencias de la salud.
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