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Related Concept Videos

Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

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The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
One type of fragmentation pattern is the cleavage of a single bond in the molecular ion. The cleavage leads to a radical and a cation. The cleavage can...
2.8K
Mass Spectrometry: Alkyne Fragmentation00:53

Mass Spectrometry: Alkyne Fragmentation

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The fragmentation of alkynes preferentially occurs at the carbon–carbon bond between the α and β carbon of the alkyne bond to generate a 3-propynyl cation (or propargyl cation). In terminal alkynes, there is the only type of fragmentation that yields the 3-propynyl cation. The unsubstituted 3-propynyl cation exhibits a peak at a mass-to-charge ratio of 39. In internal alkynes, the 3-propynyl cation is substituted. For example, 2-pentyne fragments into methyl-substituted...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation

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The fragmentation patterns observed for compounds such as carboxylic acids, esters, and amides in the mass spectra include ⍺-cleavage and McLafferty rearrangement. Fragmentation by ⍺-cleavage preferentially occurs at the carbon-carbon bond at the ⍺-position next to the carboxylic group to generate a neutral radical and a cation. Long chain compounds with hydrogen at their γ-carbon undergo McLafferty rearrangement to give a radical cation and a neutral alkene.
For example,...
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Mass Spectrometry: Cycloalkene Fragmentation00:54

Mass Spectrometry: Cycloalkene Fragmentation

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The molecular ions of cycloalkenes undergo fragmentation via a retro-Diels–Alder reaction.
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Mass Spectrometry: Aromatic Compound Fragmentation01:23

Mass Spectrometry: Aromatic Compound Fragmentation

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Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...
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Fragment-level feature fusion method using retrosynthetic fragmentation algorithm for molecular property prediction.

Qifeng Jia1, Yekang Zhang1, Yihan Wang2

  • 1School of Information Science and Technology, Nantong University, Nantong, 226001, China.

Journal of Molecular Graphics & Modelling
|February 26, 2025
PubMed
Summary

A new Fragment-level Feature Fusion Method (RFA-FFM) improves molecular property prediction for drug discovery. This AI approach enhances accuracy by integrating multi-perspective molecular representations, accelerating the development of new therapeutics.

Keywords:
Artificial IntelligenceFeature fusionFragmentationMolecular property prediction

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Area of Science:

  • Artificial Intelligence in Drug Discovery
  • Computational Chemistry
  • Machine Learning for Molecular Modeling

Background:

  • Deep learning significantly impacts drug discovery, particularly in predicting molecular properties like toxicity and blood-brain barrier (BBB) permeability.
  • Self-supervised learning (SSL), especially graph contrastive learning (GCL), offers strong generalization but current methods may alter molecular structures via data augmentation.
  • Existing single-perspective molecular representations fail to capture the full complexity of molecules.

Purpose of the Study:

  • To develop a novel method, RFA-FFM, for integrating multi-perspective molecular representations to enhance prediction accuracy.
  • To address limitations of current GCL methods by avoiding structural alterations and capturing hierarchical molecular information.

Main Methods:

  • RFA-FFM utilizes a retrosynthetic fragmentation algorithm to generate molecular fragments.
  • It employs contrastive learning on fragments from two retrosynthetic methods for detailed chemical insights.
  • The method fuses chemical information at molecular and fragment levels, creating multi-perspective representations.

Main Results:

  • RFA-FFM improved deep learning model performance in molecular property prediction, increasing ROC-AUC scores by 0.3%-2.6% across four benchmarks.
  • In hepatitis B virus dataset case studies, RFA-FFM outperformed baselines by 7%-11%.
  • RFA-FFM demonstrated a 2%-4% improvement in blood-brain barrier permeability prediction tasks compared to BPE and CC-Single algorithms.

Conclusions:

  • RFA-FFM effectively enhances molecular property prediction by integrating multi-perspective molecular representations.
  • The method shows superior performance in classification benchmarks and specific applications like BBB permeability prediction.
  • RFA-FFM represents a significant advancement in applying graph contrastive learning for accelerated and more accurate drug discovery.