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Related Concept Videos

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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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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Synthesis and Decomposition Reactions02:17

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Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Predicting Products: SN1 vs. SN202:27

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
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Diels–Alder vs Retro-Diels–Alder Reaction: Thermodynamic Factors01:31

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The Diels–Alder reaction is thermally reversible, meaning that the reaction reverts to the starting diene and dienophile under suitable temperatures. The forward reaction gives a cyclohexene derivative and is favored at low to medium temperatures. The reverse process, also called retro-Diels–Alder reaction, is a ring-opening process favored at high temperatures.
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C–C Bond Cleavage: Retro-Aldol Reaction00:57

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The reverse of the aldol addition reaction is called the retro-aldol reaction. Here, the carbon–carbon bond in the aldol product is cleaved under acidic or basic conditions to form two molecules of carbonyl compounds. The mechanism of the reaction consists of three steps.
In the first step, as depicted in Figure 1, the base deprotonates the β-hydroxy ketone at the hydroxyl group to form an alkoxide ion.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction.

Chaochao Yan1, Peilin Zhao2, Chan Lu2

  • 1Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

Biomolecules
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning framework for retrosynthesis prediction, enabling the creation of new reaction templates beyond existing limitations. This approach enhances molecule decomposition and outperforms previous methods on the USPTO-50K dataset.

Keywords:
and graph neural networkdrug discoverymachine learningreaction templaterecurrent neural networkretrosynthesis

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Organic Synthesis

Background:

  • Retrosynthesis is crucial for designing synthetic routes by decomposing molecules into building blocks.
  • Current template-based methods are limited by fixed reaction templates, hindering the discovery of novel chemical transformations.
  • Discovering new reaction templates is essential for advancing organic synthesis and drug discovery.

Purpose of the Study:

  • To develop an innovative retrosynthesis prediction framework capable of composing novel reaction templates.
  • To overcome the limitations of existing template-based methods in discovering new chemical reactions.
  • To introduce a machine learning approach for generating reaction templates in retrosynthesis.

Main Methods:

  • A novel retrosynthesis prediction framework using machine learning to compose reaction templates.
  • Development of a reactant candidate scoring model that captures atom-level transformations.
  • Utilizing the USPTO-50K dataset for training and evaluation.

Main Results:

  • The proposed method successfully composes novel reaction templates beyond the training set.
  • The framework outperforms previous methods on the USPTO-50K dataset.
  • Novel templates were generated for 15 USPTO-50K test reactions not covered by existing training templates.

Conclusions:

  • The developed framework represents a significant advancement in retrosynthesis prediction by enabling the discovery of novel reaction templates.
  • This machine learning-based approach enhances the ability to predict and discover new chemical reactions.
  • The method offers a powerful tool for computational chemistry and organic synthesis research.