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Brian Hie1, Hyunghoon Cho1, Bonnie Berger2,3

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.

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Este estudio introduce un protocolo computacional seguro para el entrenamiento de modelos de interacción fármaco-objetivo (DTI) en datos farmacológicos agrupados, lo que permite el intercambio de datos al mismo tiempo que protege la privacidad y valida los nuevos descubrimientos de DTI.

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Área de la Ciencia:

  • Biología computacional
  • Farmacología
  • Criptografía

Sus antecedentes:

  • El intercambio de datos farmacológicos para el descubrimiento de fármacos se ve obstaculizado por cuestiones de privacidad y propiedad intelectual.
  • Los métodos existentes para predecir las interacciones fármaco-objetivo (DTI) a menudo requieren acceso centralizado a los datos.
  • Los avances que salvan vidas están limitados por la incapacidad de combinar conjuntos de datos farmacológicos sensibles.

Objetivo del estudio:

  • Desarrollar un protocolo computacional seguro para el entrenamiento de modelos predictivos en datos farmacológicos agrupados.
  • Superar las barreras del intercambio de datos garantizando la confidencialidad de las drogas, los objetivos y las interacciones.
  • Permitir una predicción precisa de la DTI y el descubrimiento de nuevas interacciones sin comprometer la privacidad de los datos.

Principales métodos:

  • Aprovechando las herramientas criptográficas modernas para crear un protocolo computacional seguro y compartido.
  • Formación de un modelo predictivo para las interacciones entre fármacos (DTI) en un conjunto de datos a gran escala (>1 millón de interacciones).
  • Utilización de mecanismos de confidencialidad demostrables para todos los datos subyacentes.

Principales resultados:

  • El protocolo entrenó con éxito un modelo predictivo de DTI en cuestión de días en un conjunto de datos del mundo real.
  • El protocolo desarrollado logró una mayor precisión que los métodos de predicción DTI de última generación existentes.
  • Se descubrieron nuevos DTI utilizando el protocolo y se validaron experimentalmente mediante ensayos dirigidos.

Conclusiones:

  • El protocolo criptográfico desarrollado permite la formación segura y eficaz de los modelos de DTI en los datos agrupados.
  • Este enfoque supera las barreras críticas para el intercambio de datos en la investigación farmacológica.
  • Los descubrimientos y la metodología validados sientan las bases para una investigación biomédica cooperativa mejorada.