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関連する概念動画

Coupled Reactions01:17

Coupled Reactions

10.8K
Cellular processes such as building and breaking down complex molecules occur through stepwise chemical reactions. Some of these chemical reactions are spontaneous and release energy, whereas others require energy to proceed. Cells often couple the energy-releasing reaction with the energy-requiring one to carry out important cell functions. 
Energy in adenosine triphosphate or ATP molecules is easily accessible to do work. ATP powers the majority of energy-requiring cellular reactions....
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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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Crossed Aldol Reactions: Overview01:04

Crossed Aldol Reactions: Overview

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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.
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Crossed Aldol Reaction Using Weak Bases01:14

Crossed Aldol Reaction Using Weak Bases

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This lesson deals with the crossed aldol reaction using weak bases. The self-condensation of an aldehyde having α hydrogen is prevented by adding it slowly to a mixture of formaldehyde and weak bases like hydroxide and alkoxide. Upon slow addition of the aldehyde, the base deprotonates the α carbon of the aldehyde to form the corresponding enolate. The enolate subsequently attacks the formaldehyde to form a single crossed product. Figure 1 depicts the aforementioned reaction.
2.7K
Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction00:56

Crossed Aldol Reaction Using Strong Bases: Directed Aldol Reaction

2.8K
The reaction between two different carbonyl compounds comprising α hydrogen in the presence of a strong base like lithium diisopropylamide (LDA) to form a crossed aldol product is known as a directed aldol reaction. The directed aldol reaction is depicted in Figure 1.
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Crossing Over01:34

Crossing Over

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Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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機械学習を用いたC-Nクロスカップリングにおける反応性能の予測

Derek T Ahneman1, Jesús G Estrada1, Shishi Lin2

  • 1Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.

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

機械学習は合成反応のパフォーマンスを予測します ランダムフォレストモデルは,複雑な化学反応における収穫量を正確に予測し,より広範な合成方法の採用のために線形回帰を改善します.

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科学分野:

  • 化学について
  • コンピュータ化学
  • 化学工学

背景:

  • 機械学習 (ML) は科学研究においてますます重要になっています.
  • 合成プロセスの最適化には 化学反応の結果を予測することが重要です
  • 高通量実験 (HTE) は,MLモデルのトレーニングのための大規模なデータセットを生成します.

研究 の 目的:

  • 合成反応の性能を予測するためのMLの有用性を調査する.
  • 化学空間における従来の方法と MLアルゴリズムを比較する.
  • 合成方法の採用を促進するためのMLモデルの適用性を評価する.

主な方法:

  • 原子,分子,振動記述子を抽出しました.
  • パラジウム触媒によるブックワルド・ハートウィッグ交互結合反応をモデルシステムとして利用した.
  • HTEデータを用いた予測モデリングのためにランダムフォレストと線形回帰アルゴリズムを使用した.

主要な成果:

  • ランダムフォレストのアルゴリズムは 反応率の予測において 線形回帰を大幅に上回った.
  • MLモデルは,稀なトレーニングデータセットでも堅実なパフォーマンスを示しました.
  • サンプル外での予測は成功し,モデルの汎用性を検証した.

結論:

  • 機械学習,特にランダムフォレストは 合成反応の性能を正確に予測できます
  • このアプローチは,多次元化学空間をナビゲートし,反応を最適化するのに価値があります.
  • MLによる予測は,新しい合成方法の採用と開発を加速させることができます.