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Polypharmacological Drug-target Inference for Chemogenomics
Petra Schneider1, Gisbert Schneider1
1Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, CH-8093, Zurich, Switzerland.
Molecular Informatics
|May 26, 2018
Summary
Understanding drug interactions is key for precision medicine. This study uses computational analysis of drug-target effects to reveal drug relationships, aiding in identifying side effects and repurposing opportunities.
Area of Science:
- Pharmacology
- Computational Biology
- Drug Discovery
Background:
- Pharmacological drug actions frequently involve multi-target effects, with approved drugs often interacting with unintended 'off-targets' beyond their primary 'on-target'.
- Comprehending these complex drug interactions is crucial for advancing the development of precision medicines.
Purpose of the Study:
- To analyze functional drug-drug relationships using computational predictions of drug-target interactions.
- To identify potential off-target liabilities, drug-drug interactions, and drug repurposing opportunities.
Main Methods:
- Utilized computational predictions of drug-target interactions for 900 approved drugs.
- Represented drugs by their predicted activity fingerprints against 1158 potential targets.
- Constructed a drug relationship network based on fingerprint similarity.
Main Results:
- The network graph revealed clusters of drugs with similar predicted on- and off-target profiles.
- Identified mutual targets among drugs developed for distinct therapeutic indications.
- Facilitated the identification of potential off-target liabilities and drug-drug interactions.
Conclusions:
- Computational analysis of drug-target interactions provides a valuable approach to understanding complex pharmacological effects.
- This method aids in discovering drug repurposing opportunities and predicting potential adverse interactions.
- The findings support the design of more precise and safer therapeutics.
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