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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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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...

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Protein Target Prediction and Validation of Small Molecule Compound
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Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Pre-trained language model-based similarity relationship network approach for Drug-Target Interaction prediction.

Jilong Bian1, Limin Wei1, Shandong Yang1

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, Heilongjiang, China.

Journal of Molecular Graphics & Modelling
|May 9, 2026
PubMed
Summary

This study introduces a novel approach for drug-target interaction prediction by integrating language model features with similarity networks. The method enhances accuracy by considering relational dependencies, outperforming existing models.

Keywords:
Cross-fusion attention mechanismDrug–Target InteractionPre-trained language modelSimilarity relationship network

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Drug and protein language models show high predictive power in biological tasks.
  • Current language model approaches for drug-target interaction (DTI) prediction primarily use sequence data, neglecting crucial relational information.
  • Relational dependencies among drugs and proteins offer complementary insights for improving DTI prediction accuracy.

Purpose of the Study:

  • To develop a novel method for drug-target interaction prediction that incorporates both pre-trained language model features and relational information.
  • To enhance DTI prediction by leveraging similarity networks and a cross-fusion attention mechanism.

Main Methods:

  • Proposed a pre-trained language model-based similarity network approach for DTI prediction.
  • Combined pre-trained language model features with drug and protein similarity networks to enrich representations.
  • Utilized a cross-fusion attention mechanism to integrate similarity features and suppress redundant information.
  • Integrated similarity-based and structural features for a multi-view representation.

Main Results:

  • The proposed model consistently outperformed competitive baselines across four benchmark datasets (Human, C. elegans, BioSNAP, DrugBank).
  • Demonstrated robustness in imbalanced datasets and cold-start scenarios.
  • Highlighted the significant benefits of incorporating relational information into language model-based DTI prediction.

Conclusions:

  • The novel approach effectively integrates sequence and relational information for improved DTI prediction.
  • The method offers a robust and accurate solution for predicting drug-target interactions, even in challenging scenarios.
  • Incorporating relational dependencies is crucial for advancing language model-based DTI prediction.