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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Visualizing the drug target landscape.
Stephen J Campbell1, Anna Gaulton, Jason Marshall
1Computational Sciences Centre of Emphasis, Pfizer Global Research & Development, Ramsgate Road, Sandwich, Kent CT13 9NJ, UK.
Drug Discovery Today
|December 20, 2011
Summary
Developing integrated knowledge systems aids drug discovery by visualizing diverse data. This approach helps identify therapeutic opportunities and reduces information overload for researchers.
Area of Science:
- Biotechnology
- Computational Biology
- Drug Discovery
Background:
- Drug discovery requires integrating diverse biological, chemical, and clinical data.
- Existing tools often focus on single data types, hindering holistic interpretation.
- A unified system is needed to support decision-making in drug discovery programs.
Purpose of the Study:
- To describe the development of an integrated knowledge system for drug discovery.
- To showcase the use of visualization for interpreting complex datasets.
- To identify pharmaceutical opportunities through data integration.
Main Methods:
- Development of an organizational knowledge system.
- Integration of biological, chemical, and clinical data.
- Application of data visualization techniques.
- Analysis of disease association, druggability, competitor intelligence, genomics, and text mining data.
Main Results:
- Creation of distinct 'zones' of pharmaceutical opportunity based on therapeutic precedence.
- Effective visualization of integrated data, including disease association and druggability.
- A visual alerting mechanism to filter redundant information and reduce overload.
- Identification of opportunities in small-molecule repurposing, biotherapeutics, and gene family exploitation.
Conclusions:
- Integrated knowledge systems are crucial for advancing therapeutic hypothesis generation.
- Visualization is key to conveying the meaning of complex, integrated datasets.
- Further development of data standards, technologies, and collaboration is needed.
- The developed system aids in identifying and evaluating pharmaceutical opportunities more effectively.
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