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Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
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Sin salvaguardas, la integración IA-Biología corre el riesgo de acelerar futuras pandemias.

Dianzhuo Wang1, Marian Huot1,2, Zechen Zhang3

  • 1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, United States.

Frontiers in microbiology
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Resumen
Este resumen es generado por máquina.

Los modelos de lenguaje de proteínas (pLM) aceleran el diseño biológico para vacunas y terapias. Sin embargo, la ingeniería de proteínas impulsada por IA también presenta riesgos de doble uso, lo que requiere salvaguardas en los sistemas de biología de IA.

Palabras clave:
Bioseguridad de la bioseguridad.investigación de doble uso de interés (DURC)Inteligente Biología Automatizada Inteligente.Diseño de proteínas diseño de proteínas.Modelos de lenguaje de proteínas modelos de lenguaje de proteínas.

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Área de la Ciencia:

  • Biotecnología La biotecnología es la biotecnología.
  • La inteligencia artificial es inteligencia artificial.
  • Biología computacional Biología computacional.

Sus antecedentes:

  • La inteligencia artificial (IA) está revolucionando el diseño de la materia biológica.
  • Los modelos de lenguaje de proteínas (pLM) entrenados en vastos datos de secuencias pueden predecir, generar y optimizar proteínas.
  • Los pLM integrados en tuberías experimentales permiten un diseño biológico rápido y de circuito cerrado.

Objetivo del estudio:

  • Para mapear el progreso en la optimización de la aptitud de las proteínas impulsada por pLM.
  • Evaluar críticamente las aplicaciones de PLM en la evolución viral y los flujos de trabajo de laboratorio.
  • Proponer un marco y salvaguardas para los sistemas integrados de IA-biología.

Principales métodos:

  • Revisión de los avances recientes en las aplicaciones de PLM para la ingeniería de proteínas.
  • Análisis de la integración de PLM con flujos de trabajo experimentales, incluyendo el aprendizaje activo y la automatización.
  • Desarrollo de un marco orientado a la capacidad para los sistemas de IA-biología.

Principales resultados:

  • pLM acelera significativamente el diseño y la optimización de las proteínas funcionales.
  • Los enfoques impulsados por la IA se están aplicando a los estudios de evolución viral.
  • Los sistemas actuales de IA-biología presentan riesgos de doble uso que requieren una cuidadosa consideración.

Conclusiones:

  • La IA, particularmente los pLM, ofrece una velocidad sin precedentes en el diseño biológico, impactando en áreas como la vacuna y el descubrimiento terapéutico.
  • El rápido avance de la IA en biología requiere un marco para evaluar sistemas integrados y abordar posibles preocupaciones de doble uso.
  • Se necesita investigación para desarrollar salvaguardas para los sistemas de IA-biología tanto en las etapas de entrenamiento como de inferencia.