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

Angewandte Chemie - International Edition

Volumn 56, Issue 38, 2017, Pages 11520-11524

  Schneider, Petra ^b   Schneider, Gisbert ^a,b  

^a ETH ZURICH   (Switzerland)

^b InSilicom GmbH   (Switzerland)

Author keywords

celecoxib; drug discovery; machine learning; medicinal chemistry; off target effects

Indexed keywords

DRUG PRODUCTS; LEARNING SYSTEMS; MACHINE LEARNING;

CELECOXIB; COMPUTATIONAL MODEL; DRUG DISCOVERY; MACROMOLECULAR TARGETS; MEDICINAL CHEMISTRY; NON-STEROIDAL ANTI-INFLAMMATORY DRUGS; OFF-TARGET EFFECTS; PHARMACOLOGICALLY ACTIVE COMPOUNDS;

COMPUTATIONAL METHODS;

BIOLOGICAL PRODUCT; CELECOXIB; GLUCOCORTICOID RECEPTOR; NONSTEROID ANTIINFLAMMATORY AGENT; PHOSPHOTRANSFERASE; POTASSIUM CHANNEL; POTASSIUM CHANNEL BLOCKING AGENT; RESVERATROL;

AGONISTS; ANTAGONISTS AND INHIBITORS; CHEMISTRY; DOSE RESPONSE; DRUG DEVELOPMENT; HUMAN; METABOLISM; SOFTWARE; STRUCTURE ACTIVITY RELATION;

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;

EID: 85027123853     PISSN: 14337851     EISSN: 15213773     Source Type: Journal    
DOI: 10.1002/anie.201706376     Document Type: Article

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