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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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 Kd...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Structure-Activity Relationships and Drug Design

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Related Experiment Video

Updated: May 13, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

ConfDTI: Structure Confidence-Guided Multimodal Fusion for Drug-Target Interaction Prediction.

Jiayin Song1, Feiyan Sun1, Yilei Shu1

  • 1School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, Shandong, China.

Journal of Chemical Information and Modeling
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces ConfDTI, a framework that uses protein structure confidence scores to improve drug-target interaction prediction. By accounting for structural uncertainty, ConfDTI enhances model accuracy and generalization for drug discovery.

Related Experiment Videos

Last Updated: May 13, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

  • Computational Biology
  • Drug Discovery
  • Structural Bioinformatics

Background:

  • Protein structural information is crucial for predicting drug-target interactions (DTI).
  • Predicted protein structures have inherent uncertainty, which can negatively impact DTI prediction models.
  • Existing methods often treat predicted structures as definitive, amplifying errors and reducing model generalization.

Purpose of the Study:

  • To develop a novel framework, ConfDTI, that explicitly incorporates structural uncertainty into DTI prediction.
  • To improve the robustness and accuracy of DTI prediction by leveraging confidence scores of protein structures.
  • To enhance model generalization, particularly in cold-start scenarios.

Main Methods:

  • Proposed ConfDTI, a structural-confidence-guided multimodal framework for DTI prediction.
  • Utilized AlphaFold2-derived predicted local distance difference test (pLDDT) scores as a residue-level indicator of structural reliability.
  • Integrated pLDDT into both protein representation learning (modulating attention) and cross-modal interaction (regulating fusion).

Main Results:

  • ConfDTI demonstrated consistent outperformance against representative baselines on DrugBank, Davis, and KIBA datasets.
  • The framework showed improved predictive performance and superior cold-start generalization capabilities.
  • Ablation studies confirmed that modeling structural confidence was the key factor driving performance improvements.

Conclusions:

  • Explicitly modeling structural uncertainty using confidence scores significantly enhances DTI prediction.
  • ConfDTI offers a robust approach to mitigate the impact of unreliable structural data in DTI prediction.
  • The confidence-aware encoding and fusion strategy in ConfDTI promotes more reliable drug-target interaction modeling.