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

Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

8.4K
For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
8.4K
Catalysis02:50

Catalysis

27.2K
The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
27.2K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.1K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
4.1K
Factors Influencing the Rate of Chemical Reactions01:22

Factors Influencing the Rate of Chemical Reactions

4.3K
A variety of factors influence the rate of chemical reactions. For a chemical reaction to happen, atoms must collide with enough energy to overcome the repulsion between their electrons. This energy is called activation energy. Factors influencing the rate of reaction either lower the activation energy or increase the likelihood of a successful collision.
Concentration and Pressure:
The more particles present within a given space, the more likely those particles are to bump into one another....
4.3K
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

20.3K
Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
20.3K
Introduction to Enzymes01:22

Introduction to Enzymes

18.9K
The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that...
18.9K

You might also read

Related Articles

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

Sort by
Same author

DeepMech: a machine learning framework for chemical reaction mechanism prediction.

Chemical science·2026
Same author

Deciphering the PI3K-AKT-mTOR Signalling Pathway in Cancer: A Mechanistic Insight.

Current topics in medicinal chemistry·2026
Same author

Metal-free deoxygenative borylation of pyrazinyl ethers <i>via</i> an unusual boron-walking mechanism.

Chemical science·2026
Same author

Mechanistic Insights into Rh-Catalyzed Regio- and Enantioselective Hydroformylation of Cyclopropyl-Functionalized Trisubstituted Alkenes.

The Journal of organic chemistry·2025
Same author

<i>A priori</i> Design of [Mn(i)-Cinchona] catalyst for Asymmetric Hydrogenation of Ketones and β-Keto carbonyl Derivatives.

Chemical science·2025
Same author

Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.

Chemical science·2025

Related Experiment Video

Updated: Aug 12, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.9K

Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges.

Sukriti Singh1, Raghavan B Sunoj1,2

  • 1Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India.

Accounts of Chemical Research
|January 30, 2023
PubMed
Summary

Machine learning (ML) accelerates chemical reaction discovery by predicting outcomes from limited data. This approach uses feature engineering and learning for diverse reactions, improving yield and selectivity predictions.

More Related Videos

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

7.7K
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

2.8K

Related Experiment Videos

Last Updated: Aug 12, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.9K
Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

7.7K
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

2.8K

Area of Science:

  • Organic Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Reaction development aims to optimize yield and selectivity.
  • Chemical reactions exhibit complex, nonlinear dependencies on various factors.
  • Limited data from reaction development can be valuable for machine learning models.

Purpose of the Study:

  • To explore the application of machine learning (ML) in accelerating chemical reaction discovery.
  • To address challenges in applying ML to reaction development, particularly with small datasets.
  • To integrate ML workflows for predicting reaction outcomes and identifying promising candidates.

Main Methods:

  • Utilized feature engineering with quantum-chemically derived descriptors for predicting enantioselectivity in reactions like asymmetric hydrogenation.
  • Employed feature learning methods for predicting yield in Buchwald-Hartwig cross-coupling and deoxyfluorination.
  • Proposed a transfer learning protocol using pre-trained language models fine-tuned on specific reaction data for small-data discovery.
  • Explored deep neural network latent spaces for generative tasks to identify suitable substrates.

Main Results:

  • Feature engineering successfully predicted enantioselectivity for catalytic asymmetric hydrogenation, β-C(sp3)-H bond functionalization, and relay Heck reactions.
  • Feature learning demonstrated excellent predictive performance for yield in Buchwald-Hartwig cross-coupling and deoxyfluorination, and enantioselectivity in N,S-acetal formation.
  • Transfer learning proved effective for small-data reaction discovery (hundreds to thousands of samples).
  • Generative models using deep neural network latent spaces showed promise in identifying useful substrates.

Conclusions:

  • Machine learning, particularly with feature engineering and learning, can significantly expedite reaction discovery and optimization.
  • Transfer learning and generative approaches offer powerful strategies for tackling small-data challenges in reaction development.
  • The integration of ML holds substantial potential for advancing the field of chemical synthesis and discovery.