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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

9.1K
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,...
9.1K
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

13.1K
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,...
13.1K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.1K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.1K
Crossed Aldol Reactions: Overview01:04

Crossed Aldol Reactions: Overview

5.4K
Crossed aldol addition is the reaction between two different carbonyl compounds under acidic or basic conditions. Here, both the carbonyl compounds function as nucleophiles and electrophiles. As shown in Figure 1, such a reaction yields a mixture of products, two of which are formed via self-condensation, while the remaining two are formed via crossed-condensation. Without adjustment, the reaction's usefulness in organic chemistry is decreased.
5.4K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

2.0K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
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Reaction Mechanisms: Rate-limiting Step Approximation01:29

Reaction Mechanisms: Rate-limiting Step Approximation

100
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...
100

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Updated: May 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.0K

"機械学習によるC-Nクロスカップリングにおける反応性能の予測"に関するコメント

Kangway V Chuang1, Michael J Keiser2

  • 1Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, and Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA.

Science (New York, N.Y.)
|November 17, 2018
PubMed
まとめ
この要約は機械生成です。

C-Nクロスカップリング反応の産出量を予測する機械学習モデルが評価された. この研究では,実験的設計がモデルを検証するのに不十分であり,従来の機械学習制御では失敗していることが判明しました.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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関連する実験動画

Last Updated: May 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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科学分野:

  • 化学について
  • 機械学習
  • データサイエンス

背景:

  • 化学反応の結果を予測することは 合成化学にとって極めて重要です
  • マシン・ラーニング (ML) は,反応率を予測する可能性を秘めています.
  • 化学における信頼性の高いMLアプリケーションには,正確なモデルの検証が不可欠です.

研究 の 目的:

  • C-Nクロスカップリング反応を予測するMLモデルの有効性を評価する.
  • ランダムな特徴と化学的記述子を用いてMLモデルの有効性を評価する.

主な方法:

  • 原子,電子,振動ディスクリプタを入力機能として使用するMLモデルの適用.
  • 遡及的および前向きなテストシナリオが採用されました.
  • ランダムに評価された特徴と化学的特徴で訓練されたモデルの性能の比較.

主要な成果:

  • 実験設計では,化学的特徴で訓練されたモデルとランダムな特徴で訓練されたモデルを適切に区別することができなかった.
  • MLモデルは,検証のための古典的なコントロールに合格しなかった.
  • 化学記述子の予測力は決定的に確立されていない.

結論:

  • 現在の実験設計は,反応率の予測のためのMLモデルを検証するのに不十分です.
  • 化学におけるMLモデルの性能を確実に評価するには,実験設計のさらなる精細化が必要である.
  • この研究は,化学研究におけるMLの厳格な検証の重要性を強調しています.