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Alex M Clark

Showing results (31-40 of 45) with videos related to

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Journal of Chemical Information and Modeling|March 27, 2014
Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosisSean Ekins, Richard Pottorf, Robert C Reynolds, et al.
Journal of Medicinal Chemistry|November 22, 2014
Parallel worlds of public and commercial bioactive chemistry dataChristopher A Lipinski, Nadia K Litterman, Christopher Southan, et al.
Journal of Chemical Information and Modeling|May 22, 2015
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery DatasetsAlex M Clark, Krishna Dole, Anna Coulon-Spektor, et al.
Environmental Science & Technology|May 9, 2022
The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)Adelene Lai, Alex M Clark, Beate I Escher, et al.
Journal of Molecular Graphics & Modelling|March 31, 2007
Flexible 3D pharmacophores as descriptors of dynamic biological spaceJames H Nettles, Jeremy L Jenkins, Chris Williams, et al.
Methods in Molecular Biology (Clifton, N.J.)|April 20, 2018
Data Mining and Computational Modeling of High-Throughput Screening DatasetsSean Ekins, Alex M Clark, Krishna Dole, et al.
Nature Materials|April 20, 2019
Exploiting machine learning for end-to-end drug discovery and developmentSean Ekins, Ana C Puhl, Kimberley M Zorn, et al.
ACS Omega|February 8, 2019
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro LeadsManu Anantpadma, Thomas Lane, Kimberley M Zorn, et al.
Metallomics : Integrated Biometal Science|March 7, 2019
High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureusAlex G Dalecki, Kimberley M Zorn, Alex M Clark, et al.
Molecular Pharmaceutics|April 20, 2018
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug DiscoveryThomas Lane, Daniel P Russo, Kimberley M Zorn, et al.
Pageof 5

Showing results (31-40 of 45) with videos related to

Sort By:
Pageof 5
Journal of Chemical Information and Modeling|March 27, 2014
Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosisSean Ekins, Richard Pottorf, Robert C Reynolds, et al.
Journal of Medicinal Chemistry|November 22, 2014
Parallel worlds of public and commercial bioactive chemistry dataChristopher A Lipinski, Nadia K Litterman, Christopher Southan, et al.
Journal of Chemical Information and Modeling|May 22, 2015
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery DatasetsAlex M Clark, Krishna Dole, Anna Coulon-Spektor, et al.
Environmental Science & Technology|May 9, 2022
The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)Adelene Lai, Alex M Clark, Beate I Escher, et al.
Journal of Molecular Graphics & Modelling|March 31, 2007
Flexible 3D pharmacophores as descriptors of dynamic biological spaceJames H Nettles, Jeremy L Jenkins, Chris Williams, et al.
Methods in Molecular Biology (Clifton, N.J.)|April 20, 2018
Data Mining and Computational Modeling of High-Throughput Screening DatasetsSean Ekins, Alex M Clark, Krishna Dole, et al.
Nature Materials|April 20, 2019
Exploiting machine learning for end-to-end drug discovery and developmentSean Ekins, Ana C Puhl, Kimberley M Zorn, et al.
ACS Omega|February 8, 2019
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro LeadsManu Anantpadma, Thomas Lane, Kimberley M Zorn, et al.
Metallomics : Integrated Biometal Science|March 7, 2019
High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureusAlex G Dalecki, Kimberley M Zorn, Alex M Clark, et al.
Molecular Pharmaceutics|April 20, 2018
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug DiscoveryThomas Lane, Daniel P Russo, Kimberley M Zorn, et al.
Pageof 5