Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
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...
1.7K
Drug Discovery: Overview01:26

Drug Discovery: Overview

11.0K
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...
11.0K
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

232
Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
232
Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
14.9K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Commentary on the fundamentals and development of artificial intelligence models in the life sciences and best research practices.

Journal of computer-aided molecular design·2026
Same author

Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks.

Scientific reports·2026
Same author

Categorization of Protein Kinases by Combining Data from Cell Biology and Medicinal Chemistry Enables Further Evaluation and Differentiation of the Understudied Kinome.

Journal of medicinal chemistry·2026
Same author

Transformer Learning in Sequence-Based Drug Design Depends on Compound Memorization and Similarity of Sequence-Compound Pairs.

Molecular informatics·2026
Same author

Identifying and evaluating understudied protein kinases using biological and chemical criteria.

RSC medicinal chemistry·2025
Same author

Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.

Molecular informatics·2025
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

Video Experimental Relacionado

Updated: Jan 15, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K

Inteligencia artificial explicable para el diseño molecular en la investigación farmacéutica

Alec Lamens1,2, Jürgen Bajorath1,2

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, University of Bonn Friedrich-Hirzebruch-Allee 5/6 D-53115 Bonn Germany bajorath@bit.uni-bonn.de +49-228-7369-100.

Chemical science
|January 14, 2026
PubMed
Resumen

La IA explicable (XAI) es crucial para comprender las predicciones de aprendizaje automático (ML) en el diseño molecular. La integración del conocimiento del dominio mejora la XAI para un mejor refinamiento del modelo y diseño experimental en el descubrimiento de fármacos.

Palabras clave:
Inteligencia artificial explicableDiseño molecularDescubrimiento de fármacosAprendizaje automáticoConocimiento del dominio

Más Videos Relacionados

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.6K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

941

Videos de Experimentos Relacionados

Last Updated: Jan 15, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.6K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

941

Área de la Ciencia:

  • Inteligencia artificial
  • Diseño molecular
  • Química computacional

Sus antecedentes:

  • Los modelos de aprendizaje automático (ML), en particular el aprendizaje profundo, están avanzando en el diseño molecular.
  • La naturaleza de 'caja negra' de estos modelos de ML dificulta la comprensión y la aceptación de sus predicciones.
  • La IA explicable (XAI) es esencial para cerrar esta brecha, especialmente en la ciencia experimental.

Objetivo del estudio:

  • Examinar los desafíos y oportunidades de la XAI en el diseño molecular.
  • Evaluar los beneficios de incorporar el conocimiento específico del dominio en la XAI.
  • Discutir las limitaciones en la evaluación de modelos de lenguaje químico para el diseño molecular.

Principales métodos:

  • Revisión de los métodos XAI actuales en el contexto del diseño molecular.
  • Análisis de la integración del conocimiento específico del dominio para XAI.
  • Discusión sobre la evaluación de modelos de lenguaje químico.

Principales resultados:

  • Los métodos XAI deben proporcionar explicaciones transparentes e interpretables centradas en el ser humano.
  • El conocimiento del dominio puede refinar los modelos de ML, ayudar en el diseño experimental y respaldar la prueba de hipótesis.
  • Los métodos actuales de evaluación de modelos de lenguaje químico en el diseño molecular son limitados.

Conclusiones:

  • La XAI es vital para la aplicación práctica de ML en el diseño molecular.
  • Adaptar la XAI con experiencia en el dominio es clave para desbloquear todo su potencial.
  • Se necesita un mayor desarrollo para una evaluación sólida de las herramientas de IA en el descubrimiento de fármacos.