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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 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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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
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MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction.

Lingfeng Wang1, Yinghong Li1, Yaozheng Zhou1

  • 1Beijing University of Chemical Technology, Beijing, 100029, China.

Computational and Structural Biotechnology Journal
|June 23, 2025
PubMed
Summary

Accurately predicting drug-drug interactions (DDIs) is crucial for safe combination therapy. Our novel Multi-view Feature Embedding (MFE-DDI) method enhances DDI prediction by integrating diverse drug data, outperforming existing approaches.

Keywords:
Deep learningDrug interactionMulti-view drug featuresMultidimensional feature fusion

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Bioinformatics

Background:

  • Multidrug combination therapy is essential for complex diseases, but drug-drug interactions (DDIs) can be detrimental.
  • Accurate and rapid DDI prediction is critical for patient safety and mitigating adverse drug reactions.
  • Existing computational methods often rely on single-view drug features, limiting predictive accuracy.

Purpose of the Study:

  • To develop an advanced computational method for predicting drug-drug interactions (DDIs).
  • To enhance DDI prediction by integrating multiple drug feature representations.
  • To improve the accuracy and robustness of drug-drug interaction prediction models.

Main Methods:

  • Proposing the Multi-view Feature Embedding for drug-drug interaction prediction (MFE-DDI) model.
  • Integrating multiple drug data sources: SMILES, molecular graphs, and atom spatial semantic information.
  • Utilizing an attention-based fusion module to combine multi-view drug features effectively.

Main Results:

  • MFE-DDI significantly outperformed baseline methods across three independent datasets.
  • Model analysis confirmed the robustness and necessity of each integrated component.
  • Case studies demonstrated the practical effectiveness of MFE-DDI on newly approved drugs.

Conclusions:

  • The MFE-DDI model offers a powerful and effective approach for predicting drug-drug interactions.
  • Integrating multi-view drug features enhances the accuracy and reliability of DDI prediction.
  • This method holds promise for improving drug safety and optimizing combination therapies.