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Related Concept Videos

Protein Networks02:26

Protein Networks

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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,...
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Protein-protein Interfaces02:04

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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...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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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 discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Protein-Drug Binding: Determination Methods01:22

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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.
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Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

Xiaohan Qu1, Guoxia Du1, Jing Hu1

  • 1School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, China.

Current Computer-Aided Drug Design
|July 14, 2023
PubMed
Summary

This study introduces Graph-Drug-Target Interaction (DTI), a novel model for predicting drug-target interactions by integrating diverse data. Graph-DTI demonstrates superior performance, offering a powerful tool for drug discovery and repositioning.

Keywords:
Deep learningbioinformatics databasesclassifier design and evaluationdrug developmentfeature representationtopology.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Bioinformatics and systems biology
  • Machine learning and artificial intelligence in drug discovery

Background:

  • Accurate drug-target interaction (DTI) prediction is crucial for guiding drug discovery and development.
  • Existing machine learning methods often struggle with integrating diverse data sources and capturing complex relationships between drugs and protein targets.
  • Previous studies using heterogeneous network graphs for DTI prediction have limitations in representing neighborhood information.

Purpose of the Study:

  • To develop an end-to-end learning model, Graph-Drug-Target Interaction (Graph-DTI), for predicting DTIs.
  • To integrate various data types within a heterogeneous network, including drug-drug interactions, protein-protein interactions, drug structure similarity, and protein sequence similarity.
  • To explore automatic learning of topology-maintaining representations for drugs and targets to enhance DTI prediction accuracy.

Main Methods:

  • Construction of a heterogeneous network integrating drugs, targets, and their associated interaction and similarity data (DrugBank, HPRD, RDKit, Smith-Waterman).
  • Application of a graph neural network-inspired graph auto-encoding method to extract high-order structural information and node representations.
  • Prediction of potential DTIs using the learned representations, followed by secondary classification of the obtained samples.

Main Results:

  • The Graph-DTI model demonstrated superior performance compared to all baseline methods.
  • Performance was evaluated using the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC).
  • Graph-DTI achieved better results in both AUPR and AUC metrics, indicating enhanced prediction accuracy.

Conclusions:

  • Graph-DTI significantly outperforms existing DTI prediction methods, offering improved prediction performance.
  • The model effectively classifies drugs based on their targets and vice versa.
  • Graph-DTI serves as a powerful and more effective tool for drug research, development, and repositioning compared to previous approaches.