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

Chemical Ionization (CI) Mass Spectrometry01:21

Chemical Ionization (CI) Mass Spectrometry

The molecular ion peak of a molecule in the mass spectrum provides vital information for molecular identification. However, conventional electron impact ionization can lead to the rapid dissociation of some molecular ions before they reach the detector. A milder ionization method is required to increase the lifetime of such ionized analyte molecules. Chemical ionization (CI) is a gas-phase protonation reaction useful for mass-analyzing analyte molecules that are easily protonated to yield the...
π Molecular Orbitals of the Allyl Cation and Anion01:18

π Molecular Orbitals of the Allyl Cation and Anion

An allyl group is a three-carbon conjugated system where the sp³-hybridized allylic carbon is bonded to a CH=CH2 group via a single bond. Allyl anions can be obtained by treating propene with a strong base that can deprotonate methyl groups. Allyl cations are formed as intermediates during substitution reactions involving allylic halides. In both cases, the hybridization of the allylic carbon changes from sp3 to sp2, giving rise to a carbon chain with three sp2-hybridized carbons, each with an...
Mass Spectrometry of Amines01:15

Mass Spectrometry of Amines

In mass spectroscopy, amines undergo fragmentation to give parent ions with odd molecule weights. This observed mass spectrum follows the nitrogen rule; a molecule with an odd number of nitrogen atoms produces a molecular ion with an odd molecular weight. Amines undergo fragmentation through α cleavage, producing nitrogen-containing cations—iminium ions—and alkyl radicals. Mass spectra of aromatic and cyclic aliphatic amines exhibit strong molecular ion peaks, but acyclic aliphatic amines show...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Carbocations02:10

Carbocations

Carbocations are one of the reaction intermediates formed during several nucleophilic substitutions or elimination reactions. A carbocation is an electron-deficient species with the central carbon atom having six electrons and three bonded atoms. The central carbon in a carbocation is sp2 hybridized with trigonal planar geometry. It has an empty p orbital perpendicular to the plane of the structure that can accept electrons. Thus, carbocations act as strong electrophiles and may react with any...
Basicity of Heterocyclic Aromatic Amines01:25

Basicity of Heterocyclic Aromatic Amines

Heterocyclic amines, where the N atom is a part of an alicyclic system, are similar in basicity to alkylamines. Interestingly, the heterocyclic amine having a nitrogen atom as part of an aromatic ring has much less basicity than its corresponding alicyclic counterpart. For this reason, as presented in Figure 1, piperidine (pKb = 2.8) is significantly more basic than pyridine (pKb = 8.8).

You might also read

Related Articles

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

Sort by
Same author

Machine Learning Prediction for Fe(II) Spin-Crossover Complex in the Same Spin State Using Geometrical and Topological Descriptors.

Journal of chemical information and modeling·2026
Same author

Cyanomethylation of Aldehydes on an Electrochemical Microflow System and Utility of Machine Learning-Assisted Examination of the Reaction Conditions.

Chemistry (Weinheim an der Bergstrasse, Germany)·2025
Same author

Understanding Conformation Importance in Data-Driven Property Prediction Models.

Journal of chemical information and modeling·2025
Same author

Analog Accessibility Score (AAscore) for Rational Compound Selection.

Journal of chemical information and modeling·2024
Same author

Interleukin 9 mediates T follicular helper cell activation to promote antibody responses.

Frontiers in immunology·2024
Same author

Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets.

ACS omega·2024
Same journal

Modeling the Clustering of Fumaric/Maleic Acid with Water and Na<sup>+</sup>, Cl<sup>-</sup> Ions.

The journal of physical chemistry. A·2026
Same journal

Determining Binding Energies of Key Fluorinated Refrigerants 1,1,1,2-Tetrafluoroethane, 2,3,3,3-Tetrafluoropropene, and 3,3,3-Trifluoropropene.

The journal of physical chemistry. A·2026
Same journal

Kinetic and Mechanistic Insights into H-Abstraction and Subsequent Isomerization and Decomposition of Monoglyme and Key Combustion Intermediates.

The journal of physical chemistry. A·2026
Same journal

First-Principles Analysis of Protonation-Induced Electronic Effects in Tetrakis(<i>p</i>-aminophenyl)porphyrin (TAPP).

The journal of physical chemistry. A·2026
Same journal

Exploring the Reactivity of the CH Radical toward Nitrous Oxide in the Context of the Interstellar Medium.

The journal of physical chemistry. A·2026
Same journal

Infrared Photodissociation Spectroscopy of Benzene-V<sup>+</sup>(CO)<sub>n</sub> "Piano Stool" Cations.

The journal of physical chemistry. A·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
11:04

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides

Published on: September 7, 2019

Methyl Cation Affinity and Methyl Anion Affinity Prediction Using Uni-Mol-Based Models.

Yuto Iwasaki1, Akinori Sato1,2, Tomoyuki Miyao1,2

  • 1Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.

The Journal of Physical Chemistry. A
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Accurate machine learning models now predict organic compound reactivity using methyl cation affinity (MCA) and methyl anion affinity (MAA). These models achieve high accuracy and speed, overcoming previous limitations for synthetic chemistry applications.

More Related Videos

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)

Published on: December 20, 2010

Related Experiment Videos

Last Updated: Jun 17, 2026

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
11:04

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides

Published on: September 7, 2019

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)
17:12

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)

Published on: December 20, 2010

Area of Science:

  • Computational chemistry
  • Machine learning in chemistry
  • Organic reaction prediction

Background:

  • Predicting nucleophilicity and electrophilicity is vital for designing polar reactions in organic chemistry.
  • Methyl cation affinity (MCA) and methyl anion affinity (MAA) are key indicators, but current machine learning models lack sufficient accuracy.
  • Existing surrogate models for MCA and MAA calculations are computationally expensive and not practical for widespread use.

Purpose of the Study:

  • To develop highly accurate and efficient machine learning surrogate models for predicting methyl cation affinity (MCA) and methyl anion affinity (MAA).
  • To improve the prediction accuracy of nucleophilicity and electrophilicity indicators for organic compounds.
  • To enable fast and reliable reactivity predictions for synthetic chemistry applications.

Main Methods:

  • Developed machine learning surrogate models utilizing a pretrained Uni-Mol encoder block and a feed-forward neural network.
  • Input conformations for MCA and MAA calculations were investigated for their impact on prediction accuracy.
  • Evaluated model performance against existing molecular graph-based neural network models and robustness to different calculation protocols.

Main Results:

  • Achieved low root-mean-square errors: 8.90 kJ/mol for MCA and 10.02 kJ/mol for MAA.
  • The proposed architecture demonstrated superior performance compared to graph-based models, even without extensive pretraining.
  • Inference time was reduced to less than 0.1 seconds per compound on a single GPU.
  • Models showed robustness across different MCA/MAA calculation protocols and effectiveness with simple fine-tuning for data scarcity.

Conclusions:

  • The developed machine learning models offer a significant advancement in accurately and rapidly predicting MCA and MAA.
  • The architectural superiority and efficiency of the models make them practical tools for synthetic chemists.
  • These models overcome data scarcity issues and provide a robust solution for fast reactivity prediction in organic chemistry.