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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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Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Computational polypharmacology with text mining and ontologies.

Conrad Plake1, Michael Schroeder

  • 1Biotechnology Center of Technische Universität Dresden, Germany. conrad.plake@biotec.tu-dresden.de

Current Pharmaceutical Biotechnology
|December 8, 2010
PubMed
Summary
This summary is machine-generated.

Text mining and ontologies analyze vast scientific literature to uncover new drug-target interactions. This approach supports efficient drug discovery and development for complex diseases.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Advancements in high-throughput data generation (microarrays, next-generation sequencing) enable broader research scope.
  • Multi-target drugs offer potential for treating complex, multifactorial diseases with increased efficacy.
  • Enhanced methods allow for more systematic evaluation of drug side effects.

Purpose of the Study:

  • To explore the application of text mining and ontologies in experimental drug discovery.
  • To highlight how these computational techniques can generate novel hypotheses on drug-target interactions.
  • To review current applications of text mining and ontologies for target and drug-target discovery.

Main Methods:

  • Text mining as a high-throughput technique to extract information from scientific literature and web pages.
  • Utilizing ontologies to structure and interpret extracted data.
  • Analyzing extracted facts and indirect links to identify potential drug-target relationships.

Main Results:

  • Text mining and ontologies facilitate the generation of new hypotheses regarding drug-target interactions.
  • These methods provide a powerful means to navigate and leverage extensive scientific literature.
  • The integration of literature mining supports conventional drug discovery approaches.

Conclusions:

  • Mining scientific literature on drugs and proteins presents significant opportunities for drug development.
  • Text mining and ontologies are valuable tools for supporting the laborious and costly drug development process.
  • This approach can accelerate the identification of novel therapeutic targets and drug candidates.