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A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA.

, , , , , , , , and . Journal of Chemical Information and Modeling, 55 (3): 645-659 (2015)

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Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets., , , , , , , and . Journal of Chemical Information and Modeling, 55 (6): 1231-1245 (2015)Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis., , and . Journal of Chemical Information and Modeling, 54 (7): 2157-2165 (2014)Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015)., , , , and . Journal of Chemical Information and Modeling, 56 (7): 1332-1343 (2016)Looking Back to the Future: Predicting in Vivo Efficacy of Small Molecules versus Mycobacterium tuberculosis., , , , , and . Journal of Chemical Information and Modeling, 54 (4): 1070-1082 (2014)Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation., , and . Journal of Chemical Information and Modeling, 53 (11): 3054-3063 (2013)Machine learning models identify molecules active against the Ebola virus in vitro., , , , , and . F1000Research, (2015)A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA., , , , , , , , and . Journal of Chemical Information and Modeling, 55 (3): 645-659 (2015)Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery, , , , and . Trends in Microbiology, 19 (2): 65--74 (Feb 2, 2011)