Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

E1 Reaction: Kinetics and Mechanism02:46

E1 Reaction: Kinetics and Mechanism

15.6K
Here, in contrast to the E2 reaction mechanism, we delve into the aspects of the E1 reaction mechanism, which has two steps: rate-limiting loss of the leaving group and abstraction of the beta hydrogen by a weak base. Typically, the experimental proof for the E1 mechanism is via kinetic studies or isotope studies. While the former demonstrates the first-order kinetics—the dependence of the reaction solely on substrate concentration—the latter proves the abstraction of hydrogen only...
15.6K
Radical Reactivity: Overview01:11

Radical Reactivity: Overview

2.1K
Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired...
2.1K
E2 Reaction: Kinetics and Mechanism02:45

E2 Reaction: Kinetics and Mechanism

10.4K
SN2 substitutions and E2 eliminations of alkyl halides proceed via a concerted pathway. While the nucleophile attacks the alpha carbon in SN2 reactions, it functions as a strong base and abstracts a beta hydrogen in the E2 mechanism. The rate-limiting transition state in E2 elimination reactions is characterized by partially broken carbon–hydrogen and carbon–halogen bonds and a partially formed pi bond between the alpha and beta carbons. The beta hydrogen and halide are eliminated...
10.4K
Reactivity of Enols01:18

Reactivity of Enols

3.3K
Enols are a class of compounds where a hydroxyl group is attached to a carbon–carbon double bond, which implies that it is a vinyl alcohol. A carbonyl compound with an α hydrogen undergoes keto–enol tautomerism and remains in equilibrium with its tautomer, the enol form. Usually, the keto tautomer is present in a higher concentration than the enol tautomer due to the higher bond energy of C=O compared to C=C. Moreover, the direction of the keto–enol equilibrium is...
3.3K
Radical Chain-Growth Polymerization: Mechanism01:09

Radical Chain-Growth Polymerization: Mechanism

2.6K
The radical chain-growth polymerization mechanism consists of three steps: initiation, propagation, and termination of polymerization. The polymerization initiates when a free radical generated from the radical initiator adds to the unsaturated bond in the monomer. The unpaired electron of the free radical and one π electron in the unsaturated bond creates a σ bond between the free radical and the monomer. As a result, the other π electron in the unsaturated bond converts this...
2.6K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
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,...
8.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Regioselective synthesis of aza-saccharins <i>via</i> anionic [1,4] Fries-type rearrangement of aryl sulfonimidoyl fluorides.

Chemical science·2026
Same author

General Reaction Conditions <i>via</i> Data-driven Optimisation.

Chimia·2026
Same author

Regioselective synthesis of aza-saccharins <i>via</i> anionic [1,4] Fries-type rearrangement of aryl sulfonimidoyl fluorides.

Chemical science·2026
Same author

Hazard Assessment of Antioxidants as Contaminants of Concern.

Environmental science & technology letters·2026
Same author

Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.

Journal of the American Chemical Society·2026
Same author

Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.

Journal of chemical information and modeling·2026

Related Experiment Video

Updated: Aug 1, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

Organic reactivity from mechanism to machine learning.

Kjell Jorner1, Anna Tomberg2, Christoph Bauer3

  • 1Early Chemical Development, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.

Nature Reviews. Chemistry
|April 28, 2023
PubMed
Summary

Machine learning models are increasingly used in chemical reactivity, reducing reliance on mechanistic hypotheses. These big data approaches offer broad applicability, complementing traditional quantum mechanics for predicting reaction properties.

More Related Videos

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

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Aug 1, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
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

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Computational Chemistry
  • Chemical Informatics

Background:

  • Traditional chemical reactivity models often rely on mechanistic hypotheses.
  • Quantum-mechanical calculations provide quantitative predictions but can be computationally intensive.
  • The advent of 'big data' enables new modeling approaches in chemistry.

Purpose of the Study:

  • To provide an overview of machine learning applications in chemical reactivity.
  • To discuss the shift from mechanistic hypotheses to data-driven models.
  • To explore the integration of quantum mechanics with machine learning.

Main Methods:

  • Review of recent machine learning applications in chemical reactivity.
  • Discussion of quantum-mechanical methods for reactivity.
  • Analysis of methods augmenting or replacing quantum-based modeling with machine learning.

Main Results:

  • Machine learning models can reduce the reliance on mechanistic components in reactivity modeling.
  • Big data applications in reactivity offer broad applicability, though often qualitative.
  • A continuum of methods exists, integrating quantum-derived data into machine learning.

Conclusions:

  • Machine learning offers powerful, broadly applicable alternatives for chemical reactivity prediction.
  • Hybrid approaches combining quantum mechanics and machine learning show promise.
  • The field is moving towards data-driven models that can implicitly learn reactivity mechanisms.