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Potenciales interatómicos de aprendizaje automático en modelado biomolecular: principios, arquitecturas y

Kobchikova P P¹, Bakirov B A¹, Ryltsev R E^2,3

¹Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, 141980 Dubna, Russia.

Biophysical reviews

|January 30, 2026

Resumen

Los potenciales interatómicos de aprendizaje automático (MLIP) ofrecen una precisión cercana a la mecánica cuántica para simulaciones biomoleculares a gran escala. Estos modelos avanzados se están convirtiendo en herramientas esenciales en biología estructural y descubrimiento de fármacos.

Sus antecedentes:

Los campos de fuerza clásicos son eficientes pero carecen de precisión para sistemas complejos.
La mecánica cuántica (por ejemplo, DFT) es precisa pero computacionalmente costosa para biomoléculas grandes.
Los potenciales interatómicos de aprendizaje automático (MLIP) proporcionan un equilibrio entre precisión y eficiencia.

Conclusiones:

Los MLIP representan un avance significativo para las simulaciones atomísticas.
Están preparados para convertirse en herramientas centrales en biología estructural, enzimología y descubrimiento computacional de fármacos.
Las mejoras continuas en datos, modelos híbridos e infraestructura están acelerando su adopción práctica.

Palabras clave:

modelado biomolecular DFT campos de fuerza potenciales interatómicos de aprendizaje automático simulaciones de dinámica molecular redes neuronales

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