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Related Concept Videos

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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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

Drug-Receptor Interactions

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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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The Two-State Receptor Model01:29

The Two-State Receptor Model

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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.
The binding affinity of a drug determines its interaction with...
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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

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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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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 16, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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DrugForm-DTA: Towards real-world drug-target binding affinity model.

Ivan Khokhlov1, Anna Tashchilova1, Nikolai Bugaev-Makarovskiy1

  • 1Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Pogodinskaya Street,10, bld. 1, Moscow 119121, Russia.

Computational and Structural Biotechnology Journal
|October 6, 2025
PubMed
Summary

Drug-target affinity prediction is crucial for drug discovery. The new DrugForm-DTA model uses structure-less data and achieves experimental-level accuracy, outperforming other computational methods.

Keywords:
BindingDBDTADeep learningDrug DiscoveryDrug-target affinity predictionQSAR

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery
  • Bioinformatics and computational biology

Background:

  • Drug-target affinity (DTA) prediction is essential for efficient drug discovery.
  • Existing computational DTA methods often lack 3D protein structures, which are frequently unavailable.
  • There is a need for accurate and accessible DTA prediction tools.

Purpose of the Study:

  • To develop a novel computational model for predicting drug-target affinity (DTA).
  • To utilize structure-less representations of proteins and ligands for DTA prediction.
  • To achieve DTA prediction accuracy comparable to experimental measurements.

Main Methods:

  • Developed DrugForm-DTA, a Transformer-based neural network.
  • Employed ESM-2 for protein encoding and Chemformer for small molecule ligand encoding.
  • Trained and evaluated the model on standard benchmarks (Davis, KIBA) and a filtered BindingDB dataset.

Main Results:

  • DrugForm-DTA demonstrated superior performance on KIBA and Davis benchmarks.
  • The model achieved DTA prediction confidence comparable to single in vitro experiments.
  • DrugForm-DTA outperformed traditional molecular modeling methods in DTA prediction efficacy.

Conclusions:

  • DrugForm-DTA offers a highly accurate computational method for DTA prediction.
  • The model's performance rivals experimental measurement accuracy.
  • The study provides a valuable, filtered BindingDB dataset and a robust DTA prediction tool.