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A Computational Method for Unveiling the Target Promiscuity of Pharmacologically Active Compounds.
Journal article

A Computational Method for Unveiling the Target Promiscuity of Pharmacologically Active Compounds.

  • Schneider P inSili.com LLC, Segantinisteig 3, 8049, Zurich, Switzerland.
  • Schneider G Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
  • 2017-07-14
Published in:
  • Angewandte Chemie (International ed. in English). - 2017
celecoxib
drug discovery
machine learning
medicinal chemistry
off-target effects
Anti-Inflammatory Agents, Non-Steroidal
Biological Products
Celecoxib
Dose-Response Relationship, Drug
Drug Discovery
Humans
Phosphotransferases
Potassium Channel Blockers
Potassium Channels
Receptors, Glucocorticoid
Resveratrol
Software
Structure-Activity Relationship
English Drug discovery is governed by the desire to find ligands with defined modes of action. It has been realized that even designated selective drugs may have more macromolecular targets than is commonly thought. Consequently, it will be mandatory to consider multitarget activity for the design of future medicines. Computational models assist medicinal chemists in this effort by helping to eliminate unsuitable lead structures and spot undesired drug effects early in the discovery process. Here, we present a straightforward computational method to find previously unknown targets of pharmacologically active compounds. Validation experiments revealed hitherto unknown targets of the natural product resveratrol and the nonsteroidal anti-inflammatory drug celecoxib. The obtained results advocate machine learning for polypharmacology-based molecular design, drug re-purposing, and the "de-orphaning" of phenotypic drug effects.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/31100
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