Sean Ekins

Summary

Publications

  1. doi request reprint A collaborative database and computational models for tuberculosis drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94403, USA
    Mol Biosyst 6:840-51. 2010
  2. doi request reprint Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA
    Mol Biosyst 6:2316-24. 2010
  3. doi request reprint Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA
    Pharm Res 28:1859-69. 2011
  4. pmc Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models
    Sean Ekins
    Collaborative Drug Discovery, Burlingame, California, United States of America
    PLoS ONE 8:e63240. 2013
  5. pmc Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA
    Chem Biol 20:370-8. 2013
  6. doi request reprint Combining computational methods for hit to lead optimization in mycobacterium tuberculosis drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA
    Pharm Res 31:414-35. 2014
  7. doi request reprint Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
    J Chem Inf Model 53:3054-63. 2013
  8. doi request reprint Bottlenecks caused by software gaps in miRNA and RNAi research
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA
    Pharm Res 29:1717-21. 2012
  9. doi request reprint Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery
    Moses Hohman
    Collaborative Drug Discovery, Inc, Burlingame, CA 94403, USA
    Drug Discov Today 14:261-70. 2009
  10. doi request reprint Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA Electronic address
    Tuberculosis (Edinb) 94:162-9. 2014

Collaborators

Detail Information

Publications10

  1. doi request reprint A collaborative database and computational models for tuberculosis drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94403, USA
    Mol Biosyst 6:840-51. 2010
    ....
  2. doi request reprint Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA
    Mol Biosyst 6:2316-24. 2010
    ..Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity...
  3. doi request reprint Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA
    Pharm Res 28:1859-69. 2011
    ..Several public datasets from the Collaborative Drug Discovery Tuberculosis (CDD TB) database have been evaluated with cheminformatics approaches to validate their utility and suggest compounds for testing...
  4. pmc Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models
    Sean Ekins
    Collaborative Drug Discovery, Burlingame, California, United States of America
    PLoS ONE 8:e63240. 2013
    ..The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery...
  5. pmc Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA
    Chem Biol 20:370-8. 2013
    ..The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery...
  6. doi request reprint Combining computational methods for hit to lead optimization in mycobacterium tuberculosis drug discovery
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA
    Pharm Res 31:414-35. 2014
    ..We have used this data for building computational models as an approach to minimize the number of compounds tested...
  7. doi request reprint Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
    J Chem Inf Model 53:3054-63. 2013
    ..Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date. ..
  8. doi request reprint Bottlenecks caused by software gaps in miRNA and RNAi research
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA
    Pharm Res 29:1717-21. 2012
    ..For example a new software application could be created that provides interactive, comprehensive target analysis that leverages past datasets to lead to statistically stronger analyses...
  9. doi request reprint Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery
    Moses Hohman
    Collaborative Drug Discovery, Inc, Burlingame, CA 94403, USA
    Drug Discov Today 14:261-70. 2009
    ..We will illustrate several case studies for anti-malarial research enabled by this platform, which we suggest could be easily expanded more broadly for pharmaceutical research in general...
  10. doi request reprint Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
    Sean Ekins
    Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA Electronic address
    Tuberculosis (Edinb) 94:162-9. 2014
    ..tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization. ..