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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Antibiotic Dereplication Using the Antibiotic Resistance Platform
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Un enfoque de aprendizaje profundo para el descubrimiento de antibióticos

Jonathan M Stokes1, Kevin Yang2, Kyle Swanson2

  • 1Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

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|February 22, 2020
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Una red neuronal profunda identificó la halicina, un nuevo antibiótico eficaz contra las bacterias resistentes. Este descubrimiento impulsado por la IA amplía el arsenal de antibióticos para combatir infecciones difíciles.

Palabras clave:
Resistencia a los antibióticostolerancia a los antibióticosLos antibióticosdescubrimiento de fármacosAprendizaje automático

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Área de la Ciencia:

  • Microbiología
  • Inteligencia artificial
  • Descubrimiento de drogas

Sus antecedentes:

  • El aumento de las bacterias resistentes a los antibióticos requiere el descubrimiento de nuevos agentes antimicrobianos.
  • Los métodos convencionales de descubrimiento de antibióticos se enfrentan a desafíos significativos en la identificación de nuevos fármacos candidatos.

Objetivo del estudio:

  • Desarrollar y aplicar una red neuronal profunda para predecir moléculas con actividad antibacteriana.
  • Identificar compuestos antibacterianos nuevos y estructuralmente distintos utilizando inteligencia artificial.

Principales métodos:

  • Entrenar una red neuronal profunda en las bibliotecas químicas para predecir las propiedades antibacterianas.
  • Cribado de grandes bases de datos químicas, incluida la base de datos ZINC15, en busca de posibles candidatos a antibióticos.
  • Prueba de la eficacia de compuestos identificados, como la halicina, contra un amplio espectro de patógenos bacterianos in vitro e in vivo.

Principales resultados:

  • Descubrimiento de la halicina, una molécula estructuralmente diferente de los antibióticos existentes, que exhibe actividad bactericida de amplio espectro.
  • Tratamiento exitoso de las infecciones por Clostridioides difficile y Acinetobacter baumannii pan-resistentes en modelos murinos utilizando halicina.
  • Identificación de ocho nuevos compuestos antibacterianos de más de 107 millones de moléculas, todos estructuralmente distintos de los antibióticos conocidos.

Conclusiones:

  • Los modelos de aprendizaje profundo son herramientas efectivas para acelerar el descubrimiento de antibióticos.
  • La IA puede identificar nuevas moléculas antibacterianas con estructuras únicas, expandiendo las opciones terapéuticas contra los patógenos resistentes.
  • Este enfoque ofrece una estrategia prometedora para reponer el suministro de antibióticos en disminución.