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関連する概念動画

Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

1.6K
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
1.6K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

594
Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
594
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K
Ligand Binding Sites02:40

Ligand Binding Sites

14.8K
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...
14.8K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

196
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
196
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

14.8K
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:
14.8K

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Updated: Jan 8, 2026

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DeepKinome: 深層学習ベースの回帰モデルによる化合物とキナーゼの結合親和性の定量的予測

Yeeun Lee1, Jisu Eun2, Jinhyuk Lee2,3

  • 1Department of Genome Medicine and Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.

Frontiers in molecular biosciences
|December 19, 2025
PubMed
まとめ

DeepKinome、深層学習モデルはキナーゼ結合親和性を正確に予測する。この進歩は、キナーゼ阻害の理解を深め、複雑な化合物-タンパク質相互作用を分析することによって新しい薬を開発するのに役立つ。

キーワード:
深層学習説明可能な人工知能キナーゼ活性キナーゼ阻害予測低分子

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科学分野:

  • 生化学
  • 計算生物学
  • 創薬

背景:

  • キナーゼは細胞プロセスに不可欠であり、創薬における重要な標的です。
  • 低分子とキナーゼ間の結合親和性の予測は、複雑なデータのため困難です。

研究 の 目的:

  • 定量的キナーゼ-化合物結合親和性を予測するための深層学習モデル、DeepKinomeを開発すること。
  • DeepKinomeの性能を既存の機械学習および深層学習モデルと比較評価すること。

主な方法:

  • 20層の畳み込みニューラルネットワークベースの深層学習回帰モデル（DeepKinome）を開発しました。
  • L1000データベースの234のキナーゼと163の化合物からなるデータセットでモデルをトレーニングしました。
  • 性能は、平均二乗誤差（RMSE）、決定係数（R2）、ピアソン相関係数（PCC）、および許容区間比（AIR）を使用して評価されました。

主要な成果:

  • DeepKinomeは、5つの深層学習モデルと4つの機械学習モデルと比較して優れた性能を示しました。
  • RMSE 1.157、R2 0.535、PCC 0.743、AIR 0.570を達成しました。
  • 説明可能なAIは、既知のキナーゼリン酸化部位と相関する、予測に影響を与える重要なアミノ酸配列を特定しました。

結論:

  • DeepKinomeは、キナーゼ結合親和性を予測するための堅牢なアプローチを提示します。
  • このモデルは、キナーゼ阻害メカニズムの理解を深め、新規治療薬の開発を支援します。