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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Updated: Feb 26, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Modelado multiescala mejorado por grafos de conocimiento para la predicción de interacciones fármaco-fármaco

Jing Chen1, Qiang Deng2,3, Peimeng Zhen2,3

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

Molecular therapy. Nucleic acids
|February 25, 2026
PubMed
Resumen

La predicción de interacciones fármaco-fármaco (IFF) es vital para la seguridad del paciente. Nuestro nuevo modelo ALG-DDI integra información multiescala de fármacos, mejorando significativamente la precisión de la predicción de IFF con respecto a los métodos existentes.

Palabras clave:
Bioinformáticaaprendizaje profundointeracción fármaco-fármacografo de conocimientofusión de características multiescalacodificador de transformador

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Área de la Ciencia:

  • Farmacología y Bioinformática
  • Inteligencia Artificial en Medicina

Sus antecedentes:

  • La predicción de interacciones fármaco-fármaco (IFF) es esencial para prevenir eventos adversos de los medicamentos.
  • Los modelos actuales de aprendizaje automático y aprendizaje profundo tienen dificultades para generalizar y capturar relaciones farmacológicas integrales.

Objetivo del estudio:

  • Desarrollar un modelo avanzado, ALG-DDI, para la predicción precisa de interacciones fármaco-fármaco.
  • Integrar diversas fuentes de información sobre fármacos para un enfoque más holístico del análisis de IFF.

Principales métodos:

  • Se propuso ALG-DDI, un modelo de fusión de características multiescala.
  • Se integró información sobre atributos de fármacos, correlaciones locales (proteínas, enfermedades) e información semántica global (PrimeKG).
  • Se emplearon enmascaramiento de atributos, RGCN, GraphSAGE, ComplEx y un codificador de transformador para la representación y fusión de características.

Principales resultados:

  • ALG-DDI demostró un rendimiento superior en comparación con los métodos de vanguardia en tres conjuntos de datos.
  • Las evaluaciones exhaustivas confirmaron la eficacia del modelo, incluida la validación cruzada y los estudios de caso.
  • El modelo se extendió con éxito a la predicción de eventos de interacción fármaco-fármaco.

Conclusiones:

  • ALG-DDI captura eficazmente información multiescala de fármacos para mejorar la predicción de IFF.
  • El modelo propuesto ofrece una solución robusta para identificar posibles eventos adversos de los medicamentos.
  • Este enfoque avanza el campo de la farmacología computacional y la medicina personalizada.