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Related Concept Videos

Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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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,...
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Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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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...
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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:
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Related Experiment Video

Updated: Jan 8, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression

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
Summary
This summary is machine-generated.

DeepKinome, a deep learning model, accurately predicts kinase binding affinity. This advancement aids in understanding kinase inhibition and developing new drugs by analyzing complex compound-protein interactions.

Keywords:
deeplearningexplainable artificial intelligencekinase activitykinase inhibition predictionsmall molecules

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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Kinases are crucial for cellular processes and are key targets in drug development.
  • Predicting the binding affinity between small molecules and kinases is complex due to intricate data.

Purpose of the Study:

  • To develop a deep learning model, DeepKinome, for predicting quantitative kinase-compound binding affinity.
  • To evaluate DeepKinome's performance against existing machine learning and deep learning models.

Main Methods:

  • A 20-layer convolutional neural network-based deep learning regression model (DeepKinome) was developed.
  • The model was trained on data from 234 kinases and 163 compounds from the L1000 database.
  • Performance was assessed using root mean square error (RMSE), R-squared (R2), Pearson's correlation coefficient (PCC), and acceptance interval ratio (AIR).

Main Results:

  • DeepKinome demonstrated superior performance compared to five deep learning and four machine learning models.
  • Achieved an RMSE of 1.157, R2 of 0.535, PCC of 0.743, and AIR of 0.570.
  • Explainable AI identified key amino acid sequences influencing predictions, correlating with known kinase phosphorylation sites.

Conclusions:

  • DeepKinome presents a robust approach for predicting kinase binding affinity.
  • The model enhances understanding of kinase inhibition mechanisms and aids in the development of novel therapeutics.