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Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Ali K Abdul Raheem1,2, Ban N Dhannoon3

  • 1Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq.

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Predicting drug-target interactions (DTIs) accelerates drug discovery by identifying molecular associations. Machine learning methods offer efficient computational approaches to overcome experimental limitations in DTI prediction.

Keywords:
Machine learningchemoinformaticsdrug discoverydrug-target interactionsmedications.prediction computational models

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Drug Discovery

Background:

  • Drug-target interactions (DTIs) are crucial for modulating biological functions and form the basis of drug development.
  • Experimental methods for identifying DTIs are time-consuming and expensive, necessitating computational approaches.
  • Accurate DTI prediction can significantly enhance the efficiency and reduce the costs associated with drug discovery.

Purpose of the Study:

  • To provide a comprehensive overview of drug-target interactions as a critical initial step in drug discovery.
  • To explore the application and effectiveness of machine learning methods in predicting DTIs.
  • To review relevant literature and databases used in the field of DTI prediction.

Main Methods:

  • Review of existing literature and databases focused on drug-target interactions.
  • Exploration of computational methods for DTI prediction, including docking simulations, ligand-based approaches, and machine learning techniques.
  • Analysis of machine learning applications for predicting associations between drugs and their biological targets.

Main Results:

  • Drug-target interaction prediction is a vital area within drug discovery, impacting efficiency and cost reduction.
  • Machine learning methods present a promising avenue for accelerating DTI prediction compared to traditional experimental assays.
  • The review highlights the diverse applications of DTI prediction, including drug discovery, adverse effect prediction, and drug repositioning.

Conclusions:

  • Machine learning-based DTI prediction is a key strategy for streamlining the early stages of drug development.
  • Computational approaches, particularly machine learning, are essential for overcoming the limitations of experimental DTI identification.
  • Further research and application of these methods can significantly shorten the time to market for new therapeutics.