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Related Concept Videos

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...
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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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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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Related Experiment Video

Updated: Jan 8, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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MambaTransDTA: A Hybrid Mamba-Transformer Architecture for Accurate Drug-Target Binding Affinity Prediction.

Xinpo Lou1, Jianxiu Cai2, Qidong Liu3

  • 1Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Journal of Chemical Information and Modeling
|December 17, 2025
PubMed
Summary

MambaTransDTA, a novel hybrid deep learning model, enhances drug-target affinity prediction accuracy by integrating Mamba and Transformer architectures. This AI-driven approach improves drug discovery efficiency.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Artificial intelligence in drug discovery

Background:

  • Deep learning models show promise in drug-target affinity (DTA) prediction.
  • Existing DTA prediction models require improvements in accuracy, robustness, and generalization.

Purpose of the Study:

  • To develop a novel hybrid model, MambaTransDTA, for enhanced drug-target interaction prediction.
  • To leverage the strengths of Mamba and Transformer architectures for comprehensive DTA estimation.

Main Methods:

  • Integration of the Mamba architecture for long-range dependency capture.
  • Integration of the Transformer architecture for local interaction modeling.
  • Development of a hybrid MambaTransDTA model for DTA prediction.

Main Results:

  • MambaTransDTA achieved superior prediction accuracy on four benchmark datasets (Davis, KIBA, Metz, BindingDB).
  • Demonstrated significant relative error reductions compared to existing models: 4.3% (Davis), 3.3% (KIBA), 4.7% (Metz), and 10.5% (BindingDB).
  • Ablation studies confirmed the effectiveness of the hybrid architecture.

Conclusions:

  • MambaTransDTA significantly improves DTA prediction performance by synergistically combining Mamba and Transformer strengths.
  • The model offers a powerful tool for advancing AI-driven drug discovery.
  • The study provides accessible data and code for further research.