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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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Drug-Target Interactions: Prediction Methods and Applications.

Shanmugam Anusuya1, Manish Kesherwani2, K Vishnu Priya1

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai - 600036, Tamil Nadu, India.

Current Protein & Peptide Science
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Predicting drug-target interactions is crucial for drug discovery and repurposing. This review overviews computational methods, databases, and webservers for identifying these essential biological interactions.

Keywords:
Drug-target interactiondrug designdrug repurposingfeature based methodmachine learningpolypharmacologysemi-supervised methodsimilarity based methodsupervised method.

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Area of Science:

  • Pharmacology and Bioinformatics
  • Computational Drug Discovery

Background:

  • Identifying drug-target interactions is fundamental to understanding disease mechanisms and drug effects.
  • Accurate prediction aids in identifying novel therapeutic applications and potential adverse drug reactions.

Purpose of the Study:

  • To provide a comprehensive overview of computational methods for predicting drug-target interactions.
  • To highlight available webservers and databases for drug-target interaction data.
  • To outline the application of drug-target interactions in disease research and lead compound identification.

Main Methods:

  • Review of existing computational approaches for drug-target interaction prediction.
  • Analysis of heterogeneous biological data sources.
  • Survey of current web-based tools and databases.

Main Results:

  • Numerous computational methods have been developed leveraging diverse biological data.
  • A variety of webservers and databases are accessible for drug-target interaction research.
  • Drug-target interaction data is applicable to identifying lead compounds for various diseases.

Conclusions:

  • Computational prediction of drug-target interactions is a vital tool in modern drug discovery and repurposing.
  • The availability of data and computational tools facilitates research in this area.
  • Understanding these interactions is key to advancing therapeutic strategies.