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| About Michael Keiser (UCSF) |
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Michael J. Keiser received a BS in Computer Science from Stanford University in 2004, along with a BA in Slavic Languages & Literature, and a MA in Russian, Eastern European and Eurasian Studies. He is now a bioinformatics PhD student and NSF fellow in Brian Shoichet’s laboratory at UC San Francisco, where he developed the Similarity Ensemble Approach (SEA), a technique to relate proteins based on the statistical similarity of their ligands. He has spoken nationally and internationally on this topic, and its application to drug repositioning.
SEA is freely available as an online tool at http://sea.docking.org
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Drug Repurposing and Side-effect Elucidation by Statistical Chemical Similarity
Michael Keiser, UCSF
Chemically similar drugs often bind biologically diverse targets, yet many marketed drugs have been presumed selective for their intended targets at therapeutic concentrations. In this work, we use the Similarity Ensemble Approach (SEA) to uncover new chemical similarities of known drugs compared against a panel of 65,000 ligands organized into hundreds of target sets. Novel off-target links emerged, including the predictions that fluoxetine (Prozac), domperidone (Motilium), and tetrabenazine (Nitoman) may antagonize the beta-adrenergic, alpha1-adrenergic, and alpha2-adrenergic receptors, respectively. In addition, fluanisone and dimetholizine were both predicted to antagonize the alpha1-adrenergic and the 5-HT1A receptors. All of these prospective predictions were confirmed by experiment at nanomolar affinities. Relating drugs to receptor ligands by shared chemical patterns reveals the unexpected polypharmacology of existing drugs.
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