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Off-target networks derived from ligand set similarity
Michael J Keiser1, Jérôme Hert
1Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA.
Methods in Molecular Biology (Clifton, N.J.)
|September 4, 2009
Summary
This study introduces a novel method to map pharmacological relationships between proteins using ligand chemical similarity. The approach reveals biologically meaningful protein groupings and predicts drug off-target effects.
Area of Science:
- Pharmacology
- Bioinformatics
- Computational Biology
Background:
- Chemically similar drugs can target diverse proteins.
- Proteins with similar structures may bind different ligands.
- Bioinformatic criteria alone are insufficient to define pharmacological relationships.
Purpose of the Study:
- To develop a quantitative method for relating proteins based on ligand chemical similarity.
- To uncover hidden pharmacological relationships among drug targets.
- To predict potential drug off-target effects.
Main Methods:
- Collected and organized large datasets of drug ligands for hundreds of protein targets.
- Calculated inter-set similarity using ligand topology.
- Developed a statistical model to rank similarity scores.
- Visualized relationships using minimum spanning trees.
Main Results:
- Generated maps revealing biologically sensible groupings of protein targets.
- Identified and experimentally validated predictions of drug off-target effects.
- Demonstrated the utility of ligand-based similarity for pharmacological profiling.
Conclusions:
- Ligand chemical similarity provides a powerful approach to understanding protein-ligand interactions.
- This method can uncover non-obvious pharmacological relationships and predict drug safety.
- The technique offers a valuable tool for drug discovery and repositioning.
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