Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Coupled Reactions01:17

Coupled Reactions

10.8K
Cellular processes such as building and breaking down complex molecules occur through stepwise chemical reactions. Some of these chemical reactions are spontaneous and release energy, whereas others require energy to proceed. Cells often couple the energy-releasing reaction with the energy-requiring one to carry out important cell functions. 
Energy in adenosine triphosphate or ATP molecules is easily accessible to do work. ATP powers the majority of energy-requiring cellular reactions....
10.8K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

11.0K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
11.0K
Crossed Aldol Reactions: Overview01:04

Crossed Aldol Reactions: Overview

6.3K
Crossed aldol addition is the reaction between two different carbonyl compounds under acidic or basic conditions. Here, both the carbonyl compounds function as nucleophiles and electrophiles. As shown in Figure 1, such a reaction yields a mixture of products, two of which are formed via self-condensation, while the remaining two are formed via crossed-condensation. Without adjustment, the reaction's usefulness in organic chemistry is decreased.
6.3K
Crossed Aldol Reaction Using Weak Bases01:14

Crossed Aldol Reaction Using Weak Bases

2.7K
This lesson deals with the crossed aldol reaction using weak bases. The self-condensation of an aldehyde having α hydrogen is prevented by adding it slowly to a mixture of formaldehyde and weak bases like hydroxide and alkoxide. Upon slow addition of the aldehyde, the base deprotonates the α carbon of the aldehyde to form the corresponding enolate. The enolate subsequently attacks the formaldehyde to form a single crossed product. Figure 1 depicts the aforementioned reaction.
2.7K
Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction00:56

Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction

2.8K
The reaction between two different carbonyl compounds comprising α hydrogen in the presence of a strong base like lithium diisopropylamide (LDA) to form a crossed aldol product is known as a directed aldol reaction. The directed aldol reaction is depicted in Figure 1.
2.8K
Crossing Over01:34

Crossing Over

172.3K
Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process...
172.3K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Biocatalytic cascades enable manufacture of the macrocyclic peptide enlicitide.

Science (New York, N.Y.)·2026
Same author

Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.

ACS central science·2026
Same author

Markovnikov hydroamination of terminal alkenes by phosphine redox catalysis.

Nature·2026
Same author

Transferable enantioselectivity models from sparse data.

Nature·2026
Same author

Development, Application, and Mechanistic Interrogation of a Dual Ni Catalysis Approach to Photoredox-Based C(sp<sup>3</sup>)-C(sp<sup>3</sup>) Cross-Coupling.

Journal of the American Chemical Society·2025
Same author

Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan-Lam Coupling of Primary Sulfonamides.

ACS catalysis·2025
Same journal

Erratum for the Research Article "Detecting supramolecular organic nanoparticles during heat wave".

Science (New York, N.Y.)·2026
Same journal

Local signals, systemic decline.

Science (New York, N.Y.)·2026
Same journal

The mechanics of liver regeneration.

Science (New York, N.Y.)·2026
Same journal

Computing in a memory with physics.

Science (New York, N.Y.)·2026
Same journal

Retraction.

Science (New York, N.Y.)·2026
Same journal

Making time.

Science (New York, N.Y.)·2026
Ver todos los artículos relacionados
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: Feb 14, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

765

Predecir el rendimiento de la reacción en el acoplamiento cruzado C-N utilizando el aprendizaje automático

Derek T Ahneman1, Jesús G Estrada1, Shishi Lin2

  • 1Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.

Science (New York, N.Y.)
|February 17, 2018
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje automático predice el rendimiento de la reacción sintética. Un modelo forestal aleatorio predice con precisión los rendimientos en reacciones químicas complejas, mejorando la regresión lineal para una adopción más amplia de la metodología sintética.

Más Videos Relacionados

Synthesis of a Borylated Ibuprofen Derivative Through Suzuki Cross-Coupling and Alkene Boracarboxylation Reactions
08:56

Synthesis of a Borylated Ibuprofen Derivative Through Suzuki Cross-Coupling and Alkene Boracarboxylation Reactions

Published on: November 30, 2022

3.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Videos de Experimentos Relacionados

Last Updated: Feb 14, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

765
Synthesis of a Borylated Ibuprofen Derivative Through Suzuki Cross-Coupling and Alkene Boracarboxylation Reactions
08:56

Synthesis of a Borylated Ibuprofen Derivative Through Suzuki Cross-Coupling and Alkene Boracarboxylation Reactions

Published on: November 30, 2022

3.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Área de la Ciencia:

  • Química
  • Química computacional
  • Ingeniería Química

Sus antecedentes:

  • El aprendizaje automático (ML) es cada vez más vital en la investigación científica.
  • Predecir los resultados de las reacciones químicas es crucial para optimizar los procesos sintéticos.
  • La experimentación de alto rendimiento (HTE) genera grandes conjuntos de datos para el entrenamiento de modelos ML.

Objetivo del estudio:

  • Investigar la utilidad del ML para predecir el rendimiento de la reacción sintética.
  • Para comparar los algoritmos ML con los métodos tradicionales en el espacio químico.
  • Evaluar la aplicabilidad de los modelos ML para facilitar la adopción de metodologías sintéticas.

Principales métodos:

  • Descriptores atómicos, moleculares y vibracionales extraídos para componentes de reacción.
  • Utilizó una reacción de acoplamiento cruzado Buchwald-Hartwig catalizada por el paladio como un sistema modelo.
  • Se emplearon algoritmos de regresión lineal y bosques aleatorios para el modelado predictivo utilizando datos HTE.

Principales resultados:

  • El algoritmo de bosque aleatorio superó significativamente a la regresión lineal en la predicción del rendimiento de la reacción.
  • El modelo ML demostró un rendimiento robusto incluso con conjuntos de datos de entrenamiento escasos.
  • Se lograron predicciones exitosas fuera de la muestra, validando la generalizabilidad del modelo.

Conclusiones:

  • El aprendizaje automático, específicamente el bosque aleatorio, puede predecir con precisión el rendimiento de la reacción sintética.
  • Este enfoque es valioso para navegar por el espacio químico multidimensional y optimizar las reacciones.
  • Las predicciones basadas en el aprendizaje automático pueden acelerar la adopción y el desarrollo de nuevos métodos sintéticos.