ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Predicting new molecular targets for known drugs (2009)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2009-11-03
    Description: Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we 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. Thirty were tested experimentally, including the antagonism of the beta(1) receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H(4) receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (〈100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784146/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784146/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Keiser, Michael J -- Setola, Vincent -- Irwin, John J -- Laggner, Christian -- Abbas, Atheir I -- Hufeisen, Sandra J -- Jensen, Niels H -- Kuijer, Michael B -- Matos, Roberto C -- Tran, Thuy B -- Whaley, Ryan -- Glennon, Richard A -- Hert, Jerome -- Thomas, Kelan L H -- Edwards, Douglas D -- Shoichet, Brian K -- Roth, Bryan L -- R01 DA017204/DA/NIDA NIH HHS/ -- R01 DA017204-04/DA/NIDA NIH HHS/ -- R01 DA017204-05/DA/NIDA NIH HHS/ -- R01 MH061887/MH/NIMH NIH HHS/ -- R01 MH061887-09/MH/NIMH NIH HHS/ -- R01 MH061887-10/MH/NIMH NIH HHS/ -- U19 MH082441/MH/NIMH NIH HHS/ -- U19 MH082441-01/MH/NIMH NIH HHS/ -- U19 MH082441-010001/MH/NIMH NIH HHS/ -- U19 MH082441-019002/MH/NIMH NIH HHS/ -- U19 MH082441-019003/MH/NIMH NIH HHS/ -- U19 MH082441-02/MH/NIMH NIH HHS/ -- U19 MH082441-020001/MH/NIMH NIH HHS/ -- U19 MH082441-029002/MH/NIMH NIH HHS/ -- U19 MH082441-03/MH/NIMH NIH HHS/ -- U19 MH082441-030001/MH/NIMH NIH HHS/ -- U19 MH082441-039002/MH/NIMH NIH HHS/ -- England -- Nature. 2009 Nov 12;462(7270):175-81. doi: 10.1038/nature08506. Epub 2009 Nov 1.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Pharmaceutical Chemistry, University of California San Francisco, 1700 4th Street, San Francisco, California 94143-2550, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19881490" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Computational Biology ; Databases, Factual ; Drug Evaluation, Preclinical/*methods ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Ligands ; Mice ; Mice, Knockout ; Off-Label Use ; Pharmaceutical Preparations/*metabolism ; Receptors, Serotonin/metabolism ; *Substrate Specificity ; United States ; United States Food and Drug Administration
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    Large-scale prediction and testing of drug activity on side-effect targets (2012)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2012-06-23
    Description: Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 muM. To explore relevance, we developed an association metric 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. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3383642/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3383642/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lounkine, Eugen -- Keiser, Michael J -- Whitebread, Steven -- Mikhailov, Dmitri -- Hamon, Jacques -- Jenkins, Jeremy L -- Lavan, Paul -- Weber, Eckhard -- Doak, Allison K -- Cote, Serge -- Shoichet, Brian K -- Urban, Laszlo -- AG002132/AG/NIA NIH HHS/ -- GM71896/GM/NIGMS NIH HHS/ -- GM93456/GM/NIGMS NIH HHS/ -- P01 AG002132/AG/NIA NIH HHS/ -- R01 GM071896/GM/NIGMS NIH HHS/ -- England -- Nature. 2012 Jun 10;486(7403):361-7. doi: 10.1038/nature11159.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Novartis Institutes for Biomedical Research, Cambridge, Massachusetts 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22722194" target="_blank"〉PubMed〈/a〉
    Keywords: Blood Platelets/drug effects ; Chlorotrianisene/adverse effects/chemistry/pharmacology ; Cyclooxygenase 1/metabolism ; Cyclooxygenase Inhibitors/adverse effects/pharmacology ; Databases, Factual ; Drug Evaluation, Preclinical/*methods ; *Drug-Related Side Effects and Adverse Reactions ; Estrogens, Non-Steroidal/adverse effects/pharmacology ; Forecasting ; Humans ; Models, Biological ; Molecular Targeted Therapy/adverse effects ; Platelet Aggregation/drug effects ; Reproducibility of Results ; Substrate Specificity ; Toxicity Tests/*methods
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Comment on "Predicting reaction performance in C-N cross-coupling using machine learning" (2018)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2018
    Description: 〈p〉Ahneman 〈i〉et al〈/i〉. (Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.〈/p〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    Comment on "Predicting reaction performance in C-N cross-coupling using machine learning" (2018)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2018-11-16
    Description: Ahneman et al . (Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.
    Keywords: Chemistry, Computers, Mathematics
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...