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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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Predicción de la sección transversal de colisión compuesta habilitada por el modelo de lenguaje grande

Zeyu Zhu1, Chengyi Xie2, Shaojie Lin1

  • 1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Analytical chemistry
|September 1, 2025
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Resumen
Este resumen es generado por máquina.

HyperCCS mejora la precisión de la anotación compuesta en la espectrometría de masa de movilidad iónica utilizando modelos químicos de lenguaje grande (CLLM). Este nuevo marco mejora la predicción de la sección transversal de colisión (CCS), superando los métodos existentes para diversas moléculas.

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Área de la Ciencia:

  • Química computacional
  • Química analítica
  • La bioinformática

Sus antecedentes:

  • La sección transversal de colisión (CCS) es vital para la identificación precisa de compuestos en la espectrometría de masa de movilidad iónica (IM-MS).
  • Los métodos de predicción CCS computacionales actuales luchan con datos limitados y un manejo inadecuado de las funciones multimodal, lo que lleva a un rendimiento subóptimo.
  • La predicción precisa del CCS es esencial para la construcción de bases de datos de compuestos a gran escala para aplicaciones de IM-MS.

Objetivo del estudio:

  • Desarrollar un nuevo marco computacional, HyperCCS, para la predicción precisa de la sección transversal de colisión (CCS).
  • Aprovechar los modelos químicos de lenguaje grande (CLLM) para capturar información molecular compleja.
  • Integrar las características multimodales de manera eficaz para mejorar el rendimiento predictivo en el IM-MS.

Principales métodos:

  • Se ha perfeccionado un modelo de lenguaje químico grande (CLLM) previamente entrenado en extensas secuencias SMILES.
  • Desarrolló un módulo de fusión de características intermodales para integrar características derivadas de CLLM con otros datos heterogéneos.
  • Se ha evaluado HyperCCS en conjuntos de datos de referencia (METLIN-CCS, AllCCS2) y datos experimentales internos.

Principales resultados:

  • HyperCCS demostró una predicción robusta de CCS en varias masas moleculares, tipos de aductos y modos iónicos, superando los métodos existentes.
  • El marco resolvió con precisión los isómeros y las predicciones extrapoladas a analíticos de alta masa en datos experimentales.
  • El análisis SHAP y los estudios de ablación confirmaron la contribución significativa de las características de CLLM y el mecanismo de fusión.

Conclusiones:

  • HyperCCS ofrece un avance significativo en la predicción computacional de CCS para IM-MS.
  • La integración de los CLLM y la fusión intermodal aborda efectivamente las limitaciones de los modelos de predicción anteriores.
  • HyperCCS proporciona una herramienta computacional de alto rendimiento y adaptable para la investigación en metabolómica y biología estructural.