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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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Related Experiment Video

Updated: Jul 2, 2026

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Repositioning Drugs: A Computational Approach.

Bhawna Sharma1, Iti Chauhan2, Rohit Pandey3

  • 1Department of Pharmaceutics, Dr. K.N. Modi Institute of Pharmaceutical Education and Research, Modinagar, Ghaziabad, UP, India.

Current Drug Research Reviews
|April 23, 2025
PubMed
Summary

Computational drug repositioning efficiently finds new uses for existing drugs, reducing risk and cost. Advanced methods utilize diverse data, but challenges like data noise and sparseness persist.

Keywords:
Drug repositioningbiomedical databasescomputational approachesdata sourcesdrug discovery and developmentnoise reduction

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Area of Science:

  • Pharmacology and Bioinformatics
  • Drug Discovery and Development

Background:

  • Drug repositioning offers a cost-effective alternative to traditional drug discovery.
  • Computational methods accelerate the identification of new therapeutic indications for existing drugs.

Purpose of the Study:

  • To review computational drug repositioning approaches.
  • To highlight advancements and persistent challenges in the field.

Main Methods:

  • Review of target-based, gene-expression-based, phenome-based, and multi-omics-based computational strategies.
  • Integration of diverse data sources including biomedical databases and online health information.
  • Application of techniques incorporating drug structure and target information.

Main Results:

  • Computational drug repositioning demonstrates significant promise in identifying novel drug indications.
  • Advancements have been made in leveraging varied data sources and computational techniques.

Conclusions:

  • Computational drug repositioning is a valuable and evolving tool in pharmaceutical research.
  • Addressing challenges such as data noise, method integration, and data sparseness is crucial for future progress.