M
Michael J. Keiser
Researcher at University of California, San Francisco
Publications - 54
Citations - 6019
Michael J. Keiser is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 25, co-authored 45 publications receiving 4960 citations. Previous affiliations of Michael J. Keiser include University of New Mexico & University of California, Berkeley.
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Journal ArticleDOI
Relating protein pharmacology by ligand chemistry
Michael J. Keiser,Bryan L. Roth,Bryan L. Roth,Blaine N. Armbruster,Paul Ernsberger,John J. Irwin,Brian K. Shoichet +6 more
TL;DR: This work began with 65,000 ligands annotated into sets for hundreds of drug targets, and found that methadone, emetine and loperamide (Imodium) may antagonize muscarinic M3, α2 adrenergic and neurokinin NK2 receptors, respectively.
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Predicting new molecular targets for known drugs
Michael J. Keiser,Vincent Setola,John J. Irwin,Christian Laggner,Atheir I. Abbas,Sandra J. Hufeisen,Niels H. Jensen,Michael B. Kuijer,Roberto R. Capela de Matos,Thuy B. Tran,Ryan Whaley,Richard A. Glennon,Jérôme Hert,Kelan L. Thomas,Douglas D. Edwards,Brian K. Shoichet,Bryan L. Roth,Bryan L. Roth +17 more
TL;DR: Compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands, chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations.
Journal ArticleDOI
Large-scale prediction and testing of drug activity on side-effect targets
Eugen Lounkine,Michael J. Keiser,Steven Whitebread,Dmitri Mikhailov,Jacques Hamon,Jeremy L. Jenkins,Paul Lavan,Eckhard Weber,Allison K. Doak,Serge Côté,Brian K. Shoichet,Laszlo Urban +11 more
TL;DR: An association metric is developed to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug–target–adverse drug reaction network and may have wide application to de-risking toxicological liabilities in drug discovery.
Journal ArticleDOI
Quantifying biogenic bias in screening libraries
TL;DR: A method is developed to quantify the bias in screening libraries towards biogenic molecules and consider what is missing from screening libraries and how they can be optimized.
Journal ArticleDOI
Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors.
Rafaela Salgado Ferreira,Anton Simeonov,Ajit Jadhav,Oliv Eidam,Bryan T. Mott,Michael J. Keiser,James H. McKerrow,David J. Maloney,John J. Irwin,Brian K. Shoichet +9 more
TL;DR: A parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors illuminated the origins of docking false-negatives and false-positives.