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Generación de moléculas para el diseño de fármacos: una perspectiva de aprendizaje de grafos

Nianzu Yang1, Huaijin Wu1, Kaipeng Zeng1

  • 1School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI, Shanghai Jiao Tong University, Shanghai 200240, China.

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PubMed
Resumen
Este resumen es generado por máquina.

Esta encuesta explora el aprendizaje de grafos para el diseño de moléculas y el descubrimiento de fármacos. Categoriza métodos y discute los desafíos en el avance de la investigación farmacéutica.

Palabras clave:
descubrimiento de fármacosmodelos generativosgeneración de grafosaprendizaje de representación de grafosaprendizaje automático

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Área de la Ciencia:

  • Química computacional
  • Inteligencia artificial
  • Descubrimiento de fármacos

Sus antecedentes:

  • El aprendizaje automático, especialmente el aprendizaje de grafos, está revolucionando los campos científicos.
  • El diseño y descubrimiento de moléculas, particularmente en productos farmacéuticos, es un área de aplicación clave.
  • El diseño de novo de fármacos aprovecha técnicas computacionales avanzadas.

Objetivo del estudio:

  • Proporcionar una visión general completa de los métodos de diseño de moléculas de última generación.
  • Centrarse en el diseño de novo de fármacos que incorpora el aprendizaje profundo de grafos.
  • Categorizar las metodologías existentes y discutir las direcciones futuras de investigación.

Principales métodos:

  • Categorización de los métodos de diseño de moléculas en enfoques 'todo a la vez', 'basados en fragmentos' y 'nodo por nodo'.
  • Revisión de técnicas de aprendizaje profundo de grafos aplicadas a la generación y optimización de moléculas.
  • Identificación y discusión de conjuntos de datos públicos relevantes y métricas de evaluación.

Principales resultados:

  • Se estableció un marco de categorización claro para los métodos de diseño de moléculas de novo.
  • Se destacó el papel del aprendizaje de grafos en el avance del diseño de moléculas.
  • Se presentó una vista consolidada de los conjuntos de datos y las métricas para evaluar la generación molecular.

Conclusiones:

  • El aprendizaje de grafos presenta oportunidades significativas para acelerar el descubrimiento de fármacos.
  • Se necesita más investigación para abordar los desafíos actuales en el diseño automatizado de moléculas.
  • La evaluación estandarizada y los conjuntos de datos son cruciales para el progreso en el campo.