Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.9K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.9K
Drug Discovery: Overview01:26

Drug Discovery: Overview

12.2K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
12.2K
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

34
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
34
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

23
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
23
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

27
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...
27
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
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.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.3K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

An H3K18la-ALKBH5-Runx2-LDHA positive feedback loop promotes vascular calcification in the osteogenic phenotypic transition of vascular smooth muscle cells.

Cellular signalling·2026
Same author

Factors Associated With Hyperuricemia in Patients With Coronary Heart Disease.

Reviews in cardiovascular medicine·2026
Same author

GluD1 is localized at cholinergic synapses and is an acetylcholine receptor.

Molecular psychiatry·2026
Same author

A Molecular Playground for Spin-State Ice and Coupled Electron-Spin Dynamics.

Journal of the American Chemical Society·2026
Same author

Three-dimensional printing of multilayer stretchable electronics with inclined interconnect accesses.

Nature communications·2026
Same author

Development and validation of a machine learning-based model for assessing coronary artery disease risk in postmenopausal women: a dual-center retrospective study.

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026

Video Experimental Relacionado

Updated: Feb 24, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K

Assay2Mol: Diseño de Fármacos Basado en Modelos de Lenguaje Grandes Utilizando el Contexto de Bioensayos

Yifan Deng1,2, Spencer S Ericksen3, Anthony Gitter1,2,4

  • 1Department of Computer Sciences, University of Wisconsin-Madison.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|February 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Assay2Mol, un nuevo flujo de trabajo, desbloquea datos de cribado bioquímico para el descubrimiento de fármacos. Genera candidatos a fármacos novedosos aprendiendo de la información de ensayos existente, superando a otros métodos.

Palabras clave:
diseño de fármacosmodelos de lenguaje grandescribado bioquímicodescubrimiento de fármacosaprendizaje automáticomoléculas candidatasproteínas dianasíntesis de moléculasbioquímicadescubrimiento computacional de fármacosbioinformática

Más Videos Relacionados

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.6K

Videos de Experimentos Relacionados

Last Updated: Feb 24, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K
Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.6K

Área de la Ciencia:

  • Bioquímica
  • Descubrimiento computacional de fármacos
  • Bioinformática

Sus antecedentes:

  • Las bases de datos científicas contienen vastos datos cuantitativos y de texto.
  • Los ensayos bioquímicos criban moléculas contra dianas de enfermedades.
  • El texto no estructurado en los ensayos contiene información valiosa para el descubrimiento de fármacos.
  • Esta información está en gran medida sin explotar debido a su formato.

Objetivo del estudio:

  • Presentar Assay2Mol, un flujo de trabajo basado en modelos de lenguaje grandes.
  • Aprovechar los ensayos de cribado bioquímico existentes para el descubrimiento temprano de fármacos.
  • Desbloquear el potencial de los datos de ensayos no estructurados.

Principales métodos:

  • Assay2Mol utiliza un modelo de lenguaje grande.
  • Recupera registros de ensayos existentes para dianas similares.
  • Genera moléculas candidatas utilizando el aprendizaje en contexto a partir de los datos recuperados.

Principales resultados:

  • Assay2Mol supera los enfoques recientes de aprendizaje automático.
  • Genera eficazmente moléculas ligantes candidatas para estructuras de proteínas diana.
  • El flujo de trabajo promueve la generación de moléculas más sintetizables.

Conclusiones:

  • Assay2Mol capitaliza los ensayos de cribado bioquímico existentes.
  • Ofrece un enfoque novedoso para el descubrimiento temprano de fármacos.
  • El método mejora la generación de candidatos a fármacos viables.