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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
High-throughput electronic biology: mining information for drug discovery
William Loging1, Lee Harland, Bryn Williams-Jones
1Computational Biology Group, Pfizer, Groton, Connecticut, USA. william.t.loging@pfizer.com
Nature Reviews. Drug Discovery
|March 3, 2007
Summary
In silico methods offer powerful tools for drug discovery by integrating diverse data. Computational scientists must address data challenges to generate testable hypotheses for multiple diseases.
Area of Science:
- Computational Biology
- Bioinformatics
- Drug Discovery
Background:
- Life sciences research increasingly utilizes in silico resources.
- Effective utilization requires addressing data integration and scientific application.
Purpose of the Study:
- To describe in silico approaches for identifying testable laboratory hypotheses.
- To address common challenges in computational drug discovery.
- To highlight flexible, high-throughput techniques applicable across diseases.
Main Methods:
- Focus on in silico techniques independent of wet-lab experimentation.
- Emphasis on flexible and high-throughput methodologies.
- Application across multiple disease areas.
Main Results:
- In silico approaches can generate testable hypotheses for laboratory validation.
- Flexible, high-throughput methods facilitate independent research initiation.
- These computational techniques are applicable to diverse disease contexts.
Conclusions:
- In silico techniques significantly contribute to the drug discovery pipeline.
- Addressing data integration is crucial for realizing the potential of computational resources.
- Collaboration between computational science and disease research is essential.
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