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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
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Global optimization-based inference of chemogenomic features from drug-target interactions
Songpeng Zu1, Ting Chen2, Shao Li1
1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.
Bioinformatics (Oxford, England)
|March 31, 2015
Summary
A new computational method, GIFT, globally analyzes drug-target interactions by considering substructure-domain relationships simultaneously. This approach improves prediction accuracy and offers new insights for drug discovery and repositioning.
Area of Science:
- Computational chemistry
- Cheminformatics
- Drug discovery
Background:
- Understanding chemogenomic drug-target interactions is crucial for fragment-based drug discovery and drug repositioning.
- Previous methods analyzed interactions locally, potentially leading to high false-positive rates due to ignored competitive effects.
Purpose of the Study:
- To develop a computational method for evaluating drug-target interactions with improved predictive power.
- To analyze substructure-domain interactions globally, considering combined effects of chemical substructures.
Main Methods:
- A statistical model, Global optimization-based InFerence of chemogenomic features from drug-Target interactions (GIFT), was developed.
- GIFT analyzes all substructure-domain contributions to drug-target interactions simultaneously, including combinations of chemical substructures.
Main Results:
- GIFT demonstrated superior interpretive performance and good recovery of drug-target interactions compared to previous methods.
- 81% of 53 known drug-domain interactions were accurately predicted by GIFT.
- 15 out of 18 experimentally validated drug-target structures (from top 100 predicted interactions) confirmed GIFT's predictions.
Conclusions:
- GIFT provides a global chemogenomic inference approach for analyzing drug-target interactions.
- The method offers novel insights into drug-target relationships and has potential applications in drug repositioning, identifying new therapeutic activities (e.g., anticancer properties).
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