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関連する概念動画

Ligand Binding Sites02:40

Ligand Binding Sites

13.2K
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...
13.2K
Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.3K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.4K

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Updated: Sep 10, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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ACLPred:説明可能な機械学習と抗癌リガンド予測のためのツリーベースのアンサンブルモデル

Arvind Kumar Yadav1, Jun-Mo Kim2

  • 1Functional Genomics & Bioinformatics Laboratory, Department of Animal Science and Technology, Chung-Ang University, Anseong, 17546, Gyeonggi-do, Republic of Korea.

Scientific reports
|August 25, 2025
PubMed
まとめ
この要約は機械生成です。

機械学習 (ML) は,分子特性を分析することによって,新しい抗癌薬の発見を加速します. 新しいツールであるACLPredは,Light Gradient Boosting Machine (LGBM) を使用して,潜在的な抗がん化合物を正確に予測し,時間とリソースを節約します.

キーワード:
抗癌リガンド癌について機械学習を統合するマルチステップ機能の選択

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科学分野:

  • コンピュータ化学
  • 化学情報学
  • 薬剤開発における機械学習

背景:

  • 癌の発生率が増加しているため,新しい治療薬が必要になります.
  • 伝統的な実験薬のスクリーニングは 資源を大量に消費します
  • マシン・ラーニングは 抗がん物質の特定に 迅速かつ費用対効果の高い 代替手段を提供します

研究 の 目的:

  • 小分子抗がん活性を予測するための機械学習モデルを開発し,検証する.
  • 抗癌特性を有する重要な分子特性を特定する.
  • 研究者が潜在的薬剤候補をスクリーニングするためのアクセシブルなツールを作成します.

主な方法:

  • 既知の抗癌および非抗癌化合物の分子記述子を用いた分類モデルを訓練する.
  • 多段階の特徴選択を適用して,有意な分子記述者を特定する.
  • Light Gradient Boosting Machine (LGBM) を含む様々な機械学習アルゴリズムの採用と評価
  • SHAPLEY ADDITIVE EXPLANATIONS (SHAP) を利用してモデルを解釈する.

主要な成果:

  • LGBMモデルは90.33%の予測精度と97.31%のAUROCを達成しました.
  • 開発されたツールであるACLPredは,既存の方法よりも優れた予測精度と一般化性を示しました.
  • SHAP分析では,トポロジカルな分子特性がモデルの予測に大きく影響したことが示された.

結論:

  • 機械学習,特にACLPredで実装されたLGBMアルゴリズムは,潜在的な抗癌化合物を特定するための有効で正確な方法を提供します.
  • ACLPredは 抗がん剤の発見を加速するための ユーザーフレンドリーでオープンソースのソリューションを提供します
  • トポロジカルな特徴は 抗がん活性を予測するのに不可欠であり 将来の薬剤設計の洞察を提供します