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関連する概念動画

Valence Bond Theory02:42

Valence Bond Theory

11.4K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
11.4K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

31.1K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
31.1K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

24.6K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
24.6K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.3K
VSEPR Theory for Determination of Electron Pair Geometries
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Ligand Binding Sites02:40

Ligand Binding Sites

15.4K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
15.4K
Ligand Binding Sites02:40

Ligand Binding Sites

9.0K
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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複雑なリガンド-金属アーキテクチャを予測するためのハイブリッド計算戦略

Galymzhan Moldagulov1,2, Kisung Lee1, Sanzhar Nurgaliyev1

  • 1Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS), Ulsan, Republic of Korea.

Angewandte Chemie (International ed. in English)
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PubMed
まとめ
この要約は機械生成です。

この研究では,機械学習 (ML) を用いたハイブリッドコンピューティング方式を導入し,金属-リガンドの協調パターンを予測します. ケンブリッジ構造データベース (CSD) で訓練されたMLアルゴリズムは,多様なリガンドと金属の複雑な協調を正確に予測します.

キーワード:
化学情報学 化学情報学調整モード 調整モード機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.ニューラルネットワークはニューラルネットワークです.オーガニック・メタリック

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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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科学分野:

  • 計算化学はコンピュータ化学である.
  • マテリアルサイエンス 材料科学
  • 化学情報科学は,化学情報科学である.

背景:

  • 金属-リガンドの協調を予測することは,金属複合体や触媒の設計において極めて重要です.
  • リンガンドは多数の協調モードを示し,化学者に課題を与える.
  • 既存の方法は,複雑な調整パターンで苦労しています.

研究 の 目的:

  • 複雑な金属-リガンドの協調パターンを予測するための計算アプローチを開発する.
  • 幅広いリガンドと金属に適用できる汎用的なモデルを作成する.
  • 化学者にアクセシブルなツールを提供すること.

主な方法:

  • 機械学習 (ML) と知識に基づく規則を組み合わせたハイブリッドコンピューティングのアプローチです.
  • ケンブリッジ構造データベース (CSD) のデータでMLアルゴリズムをトレーニングする.
  • 調整パターンの予測モデルを開発する.

主要な成果:

  • MLモデルは,様々なリガンドと金属の複雑な協調パターンを成功裏に予測します.
  • このアプローチは,ヘミラビル,ハプティック,および高密度リガンドを含む多様なリガンドタイプを扱う.
  • このモデルは,異なる金属の酸化状態において有効である.

結論:

  • 開発されたハイブリッドMLアプローチは,金属-リガンドの協調を予測するための堅牢なソリューションを提供します.
  • このツールは,金属複合体と触媒の合理的な設計を強化します.
  • アルゴリズムはRDKitと公開のWebポータルで利用できます.