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BOLD-GPCRs: Una aplicación impulsada por Transformer para predecir la bioactividad de ligandos y efectos mutacionales

Davide Provasi¹, Kirill Konovalov¹, Nicholas Riina²

¹Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.

Journal of chemical information and modeling

|January 14, 2026

Resumen

BOLD-GPCRs, un marco de aprendizaje profundo, mejora el descubrimiento de fármacos al predecir con precisión la bioactividad de ligandos de receptores acoplados a proteínas G (GPCR). Esta herramienta ayuda a identificar terapéuticas para GPCR subestudiados.

Área de la Ciencia:

Farmacología
Biología Computacional
Descubrimiento de Fármacos

Sus antecedentes:

Los receptores acoplados a proteínas G (GPCR) son dianas farmacológicas cruciales involucradas en numerosas enfermedades.
Los métodos tradicionales de descubrimiento de fármacos tienen dificultades con los GPCR que carecen de datos extensos de ligandos o estructurales.
Se necesitan estrategias avanzadas para la predicción precisa de la bioactividad de ligandos en toda la familia de GPCR, particularmente para subtipos subestudiados.

Objetivo del estudio:

Presentar BOLD-GPCRs, un marco de aprendizaje profundo para la predicción mejorada de la bioactividad de ligandos en GPCRs de clase A.
Proporcionar una interfaz web fácil de usar para acceder al marco BOLD-GPCRs.
Aprovechar el aprendizaje por transferencia y los conjuntos de datos curados para mejorar el descubrimiento de fármacos de GPCR.

Principales métodos:

Desarrollado BOLD-GPCRs (BERT-Optimized Ligand Discovery for GPCRs) utilizando aprendizaje profundo.
Se integraron clasificadores de redes neuronales densas con modelos de lenguaje de proteínas basados en transformadores.
Se utilizaron conjuntos de datos curados de ligandos de GPCR de clase A, secuencias de receptores y mutaciones.

Principales resultados:

BOLD-GPCRs demuestra un sólido rendimiento predictivo para la bioactividad de ligandos en GPCRs de clase A.
El marco predice con precisión los efectos de las mutaciones del receptor en la actividad del ligando.
Logró resultados sólidos, especialmente para subtipos de GPCR poco caracterizados.

Conclusiones:

BOLD-GPCRs es una herramienta valiosa para mejorar el descubrimiento de ligandos de GPCR.
El marco muestra un potencial significativo para identificar terapéuticas dirigidas a GPCR subestudiados.
Los enfoques de aprendizaje profundo ofrecen soluciones avanzadas para el desarrollo de fármacos dirigidos a GPCR.

Palabras clave:

GPCR aprendizaje profundo descubrimiento de fármacos predicción de bioactividad modelado de lenguaje de proteínas BOLD-GPCRs

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