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Regresión Simbólica Jerárquica No Supervisada para Modelado de Propiedades Interpretables en Sistemas Complejos

Siyu Lou1,2, Chengchun Liu3, Dongxiao Zhang2

  • 1School of computer science, Shanghai Jiao Tong University, Shanghai, P.R. China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 7, 2026
PubMed
Resumen
Este resumen es generado por máquina.

La Regresión Simbólica Jerárquica No Supervisada (UHSR) ofrece un enfoque de IA interpretable para el análisis químico, que vincula con éxito las estructuras moleculares con el comportamiento cromatográfico en la cromatografía en capa fina (TLC) y genera confianza en los químicos.

Palabras clave:
TLCIA explicablepolaridad molecularestructura molecularregresión simbólica

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Área de la Ciencia:

  • Inteligencia Artificial
  • Quimioinformática
  • Química Analítica

Sus antecedentes:

  • Los modelos de IA sobresalen en la predicción del análisis químico, pero a menudo carecen de interpretabilidad.
  • La cromatografía en capa fina (TLC) es vital para analizar la polaridad molecular.
  • Se necesita IA explicable para generar confianza en los modelos químicos predictivos.

Objetivo del estudio:

  • Introducir la Regresión Simbólica Jerárquica No Supervisada (UHSR) como una solución de IA interpretable.
  • Desarrollar un modelo que mantenga un rendimiento predictivo competitivo.
  • Demostrar la capacidad de la UHSR para obtener información químicamente intuitiva.

Principales métodos:

  • La UHSR destila automáticamente los índices de retención de los datos de TLC.
  • La UHSR descubre ecuaciones explicables que vinculan las estructuras moleculares con el comportamiento cromatográfico.
  • Se evaluó la adaptabilidad del modelo a otras tareas de predicción de propiedades.

Principales resultados:

  • La UHSR derivó con éxito ecuaciones concisas y precisas para la predicción de polaridad a partir de datos de TLC.
  • Los químicos expertos expresaron mayor confianza en la UHSR en comparación con los modelos tradicionales.
  • El método mostró adaptabilidad más allá de la predicción de polaridad molecular.

Conclusiones:

  • La UHSR proporciona una alternativa potente e interpretable para el modelado predictivo químico.
  • La IA explicable en química puede mejorar la confianza y la utilidad del modelo.
  • La UHSR tiene una amplia aplicabilidad en quimioinformática y química analítica.