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Tokenización para Modelos Fundacionales Moleculares

Alexius Wadell1,2, Anoushka Bhutani1,2, Venkatasubramanian Viswanathan1,2

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.

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|January 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los modelos moleculares fundacionales avanzan la ciencia, pero los tokenizadores limitados obstaculizan el progreso. Los nuevos tokenizadores Smirk y Smirk-GPE ofrecen una cobertura completa, permitiendo aplicaciones más amplias en química y más allá.

Palabras clave:
tokenizaciónmodelos moleculares fundacionalesrepresentación SMILESSmirkSmirk-GPEinteligencia artificial en químicapredicción de propiedades moleculares

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Área de la Ciencia:

  • Quimioinformática y química computacional
  • Inteligencia artificial en el descubrimiento científico
  • Representación y modelado molecular

Sus antecedentes:

  • Los modelos fundacionales basados en texto son cruciales para el descubrimiento científico, particularmente en el diseño molecular.
  • Los modelos moleculares fundacionales existentes están limitados por tokenizadores de vocabulario cerrado, lo que restringe su capacidad para representar el espacio molecular completo.
  • Una evaluación sistemática de 35 tokenizadores reveló brechas significativas en la cobertura de la representación SMILES.

Objetivo del estudio:

  • Evaluar sistemáticamente la cobertura de los tokenizadores moleculares existentes.
  • Evaluar el impacto de la elección del tokenizador en la predicción de propiedades moleculares.
  • Desarrollar nuevos tokenizadores con cobertura integral de las representaciones moleculares.

Principales métodos:

  • Se evaluaron 35 tokenizadores, incluidos 20 específicos de química, para la cobertura de la representación SMILES.
  • Se introdujeron modelos de lenguaje n-gram como sustitutos para evaluar el impacto del tokenizador.
  • Se preentrenaron y ajustaron 18 codificadores estilo RoBERTa para la predicción de propiedades moleculares.
  • Se desarrollaron dos nuevos tokenizadores, Smirk y Smirk-GPE, con cobertura completa de OpenSMILES.

Principales resultados:

  • Se identificaron brechas significativas en la cobertura de los tokenizadores existentes para SMILES.
  • Se demostró la efectividad de los modelos de lenguaje n-gram como sustitutos para la evaluación de tokenizadores.
  • Se mostró la capacidad de los codificadores preentrenados estilo RoBERTa para la predicción de propiedades moleculares.
  • Se introdujeron los tokenizadores Smirk y Smirk-GPE que integran grados de libertad nucleares, electrónicos y geométricos.

Conclusiones:

  • Los tokenizadores existentes cubren inadecuadamente el espacio molecular, lo que requiere enfoques de vocabulario abierto.
  • Los tokenizadores propuestos Smirk y Smirk-GPE proporcionan una cobertura completa de OpenSMILES y permiten aplicaciones más amplias.
  • Se destaca la necesidad de puntos de referencia químicamente diversos y modelado de vocabulario abierto en la quimioinformática.