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Related Concept Videos

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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Drug-Receptor Bonds01:25

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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Drug-Receptor Interactions01:29

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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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.
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The Two-State Receptor Model01:29

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Related Experiment Video

Updated: Sep 7, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Explainable deep drug-target representations for binding affinity prediction.

Nelson R C Monteiro1, Carlos J V Simões2, Henrique V Ávila3

  • 1Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. nelsonrcm@dei.uc.pt.

BMC Bioinformatics
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

This study shows that deep learning models using convolutional neural networks (CNNs) can reliably predict drug-target interactions by identifying key binding regions. The explainable AI approach validates the model's decision-making process in drug discovery.

Keywords:
Binding affinityConvolutional neural networkDrug–target interactionExplainable deep learning

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Artificial intelligence in drug discovery

Background:

  • Computational methods have advanced drug discovery, but often lack explainability in deep learning models.
  • Convolutional Neural Networks (CNNs) are explored for their ability to identify critical binding sites and motifs in drug-target interactions.
  • This research addresses the need for interpretable deep learning in predicting drug-target binding affinity.

Purpose of the Study:

  • To explore the reliability of CNNs in identifying relevant binding regions for drug-target interactions.
  • To assess the significance of deep representations extracted by CNNs through explainability.
  • To validate an end-to-end deep learning architecture for predicting binding affinity and interaction strength.

Main Methods:

  • Utilized an end-to-end deep learning architecture for binding affinity prediction.
  • Employed CNNs to automatically extract discriminating deep representations from 1D sequential and structural data.
  • Integrated explainability methods to identify input regions most influential in the model's predictions.

Main Results:

  • Deep representations from CNNs effectively predicted drug-target interactions, identifying key binding regions.
  • CNNs highlighted features in interaction-relevant regions with significant positive influence on predictions.
  • The end-to-end model outperformed baseline approaches in binding affinity prediction and interaction strength ranking.

Conclusions:

  • The study validates the applicability of end-to-end deep learning in drug discovery beyond traditional protein-ligand structural analysis.
  • Demonstrated the reliability of deep representations from CNNs through explainable AI.
  • Highlights the potential of interpretable deep learning for advancing novel drug lead identification.