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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,...
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The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...
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Introduction to Enzyme Kinetics01:19

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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.
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Enzyme Kinetics01:19

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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
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As depicted in the figure below, the unsymmetrical ketones can form two possible enolates: less substituted or more substituted enolates. Usually, the thermodynamic enolates are formed from the more substituted α-carbon atom, while the kinetic enolates are formed faster by deprotonation from the less substituted position. The thermodynamic enolates have lower energy, so they are more stable. But the energy required to form kinetic enolates is less.
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E1 Reaction: Kinetics and Mechanism02:46

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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...
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Related Experiment Video

Updated: Apr 28, 2026

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
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Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks.

Yu Harabuchi1,2, Ruben Staub1, Min Gao1

  • 1Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.

ACS Central Science
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for understanding asymmetric catalysis. It reveals complex reaction pathways and key intermediates, enabling the design of more efficient catalysts.

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Area of Science:

  • Computational Chemistry
  • Catalysis Science
  • Organic Chemistry

Background:

  • Asymmetric catalysts control chemical reactions but their complex mechanisms are challenging to model.
  • Current computational methods often oversimplify selectivity-determining steps and transition states.

Purpose of the Study:

  • To develop a more comprehensive computational framework for analyzing asymmetric catalysis.
  • To explore reaction mechanisms and predict enantioselectivity beyond conventional approaches.
  • To accelerate the rational design of highly reactive and enantioselective catalysts.

Main Methods:

  • Construction of a reaction path network capturing kinetically accessible regions of the potential energy surface.
  • Development and application of a delta-learning neural network potential (ΔNNP) for accurate energy calculations.
  • Utilizing the ΔNNP-based single component-artificial force induced reaction (NNP/AFIR) method to generate extensive reaction path data.
  • Performing kinetic simulations and traffic volume analysis on the generated reaction network.

Main Results:

  • A reaction path network with 48,463 paths was constructed for an imidodiphosphorimidate organocatalyst.
  • The ΔNNP achieved DFT-level accuracy, significantly outperforming the GFN2-xTB baseline.
  • Kinetic simulations identified numerous energetically competitive pathways, including asynchronous concerted and stepwise mechanisms.
  • Traffic analysis highlighted the kinetic importance of intermediates with low yields.

Conclusions:

  • The NNP/AFIR approach offers a powerful tool for in-depth mechanistic understanding of asymmetric catalysis.
  • This method facilitates the rational design of novel asymmetric catalytic systems with improved reactivity and enantioselectivity.
  • The study moves beyond Boltzmann distribution assumptions to capture the full complexity of catalytic reaction landscapes.