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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.2K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.2K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

654
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
654
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.4K
VSEPR Theory for Determination of Electron Pair Geometries
46.4K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

615
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
615
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

299
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
299
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

379
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
379

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Integration of DOPtools and CADS in a Web-Based User Interface for Structural Descriptor Calculation, Model Optimization, and Prediction.

Journal of chemical information and modeling·2026
Same author

Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.

Molecules (Basel, Switzerland)·2026
Same author

Two-Stage Probability-Enhanced Regression on Property Matrices and LLM Embeddings Enables State-of-the-Art Prediction of Gene Knockdown by Modified siRNAs.

International journal of molecular sciences·2025
Same author

Predictive modeling of visible-light azo-photoswitches' properties using structural features.

Journal of cheminformatics·2025
Same author

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis.

Chemistry (Weinheim an der Bergstrasse, Germany)·2023
Same author

Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts.

Journal of chemical information and modeling·2023

Video Experimental Relacionado

Updated: Mar 1, 2026

A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy
09:32

A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy

Published on: January 30, 2019

15.2K

Transformer basado en grafos para predecir el coeficiente de partición octanol-agua

Vyacheslav Grigorev1, Nikita Serov2, Timur Gimadiev1,2

  • 1A.M. Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlyovskaya Str, Kazan, 420008, Russia.

Journal of cheminformatics
|February 27, 2026
PubMed
Resumen

Desarrollamos GraphormerLogP, un modelo de aprendizaje profundo para predecir la lipofilicidad de fármacos (logP). Logra una alta precisión en grandes conjuntos de datos, lo que ayuda al descubrimiento de nuevos fármacos.

Palabras clave:
Redes neuronales de grafosLipofilicidadTransformerlogP

Más Videos Relacionados

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.7K

Videos de Experimentos Relacionados

Last Updated: Mar 1, 2026

A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy
09:32

A New Straightforward Method for Lipophilicity logP Measurement using 19F NMR Spectroscopy

Published on: January 30, 2019

15.2K
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.7K

Sus antecedentes:

  • La lipofilicidad (logP) es crucial para el comportamiento de los fármacos, ya que afecta la solubilidad, la permeabilidad y el metabolismo.
  • La predicción precisa de logP es vital para la selección eficiente de candidatos a fármacos.
  • Los modelos de aprendizaje profundo basados en grafos muestran ser prometedores para la predicción de propiedades moleculares.

Conclusiones:

  • GraphormerLogP ofrece una solución de alto rendimiento para la predicción de logP en el descubrimiento de fármacos.
  • El conjunto de datos GLP curado proporciona un recurso valioso para avanzar en la investigación de predicción de lipofilicidad.
  • El aprendizaje profundo basado en grafos, en particular con Graphormer, muestra un potencial significativo para tareas de predicción de propiedades moleculares.