Three-Dimensional Quantitative Structure-Permeability Relationship Analysis for a Series of Inhibitors of Rhinovirus Replication. - Sean Ekins; Gregory L. Durst; Robert E. Stratford; David A. Thorner; Richard A. Lewis; Richard J. Loncharich; James H. Wikel (J. Chem. Inf. Comput. Sci., 2001)

Towards a new age of virtual ADME/TOX and multidimensional drug discovery. - Sean Ekins; Bruno Boulanger; Peter W. Swaan; Maggie A. Z. Hupcey (J. Comput. Aided Mol. Des., 2002)

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR. - Sean Ekins; Sandhya Kortagere; Manisha Iyer; Erica J. Reschly; Markus A. Lill; Matthew R. Redinbo; Matthew D. Krasowski (PLoS Comput. Biol., 2009)

Why Open Drug Discovery Needs Four Simple Rules for Licensing Data and Models. - Antony J. Williams; John Wilbanks; Sean Ekins (PLoS Comput. Biol., 2012)

TB mobile: a mobile app for anti-tuberculosis molecules with known targets. - Sean Ekins; Alex M. Clark; Malabika Sarker (J. Cheminformatics, 2013)

Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation. - Sean Ekins; Joel S. Freundlich; Robert C. Reynolds (J. Chem. Inf. Model., 2013)

Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening. - Liying Zhang; Denis Fourches; Alexander Sedykh; Hao Zhu 012; Alexander Golbraikh; Sean Ekins; Julie Clark; Michele C. Connelly; Martina Sigal; Dena Hodges; Armand Guiguemde; R. Kiplin Guy; et al. (J. Chem. Inf. Model., 2013)

Collaboration for rare disease drug discovery research. - Nadia K. Litterman; Michele Rhee; David C. Swinney; Sean Ekins (F1000Research, 2014)

Bigger data, collaborative tools and the future of predictive drug discovery. - Sean Ekins; Alex M. Clark; S. Joshua Swamidass; Nadia K. Litterman; Antony J. Williams (J. Comput. Aided Mol. Des., 2014)

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0. - Alex M. Clark; Malabika Sarker; Sean Ekins (J. Cheminformatics, 2014)

Using cheminformatics to predict cross reactivity of "designer drugs" to their currently available immunoassays. - Matthew D. Krasowski; Sean Ekins (J. Cheminformatics, 2014)

Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis. - Sean Ekins; Joel S. Freundlich; Robert C. Reynolds (J. Chem. Inf. Model., 2014)

Looking Back to the Future: Predicting in Vivo Efficacy of Small Molecules versus Mycobacterium tuberculosis. - Sean Ekins; Richard Pottorf; Robert C. Reynolds; Antony J. Williams; Alex M. Clark; Joel S. Freundlich (J. Chem. Inf. Model., 2014)

Computational Prediction and Validation of an Expert's Evaluation of Chemical Probes. - Nadia K. Litterman; Christopher A. Lipinski; Barry A. Bunin; Sean Ekins (J. Chem. Inf. Model., 2014)

Ten Simple Rules of Live Tweeting at Scientific Conferences. - Sean Ekins; Ethan O. Perlstein (PLoS Comput. Biol., 2014)

Machine learning models identify molecules active against the Ebola virus in vitro. - Sean Ekins; Joel S. Freundlich; Alex M. Clark; Manu Anantpadma; Robert A. Davey; Peter B. Madrid (F1000Research, 2015)

Finding small molecules for the 'next Ebola'. - Sean Ekins; Christopher Southan; Megan Coffee (F1000Research, 2015)

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies. - Sonya M. Hanson; Sean Ekins; John D. Chodera (J. Comput. Aided Mol. Des., 2015)

Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data. - Alex M. Clark; Antony J. Williams; Sean Ekins (J. Cheminformatics, 2015)

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. - Alex M. Clark; Krishna Dole; Anna Coulon-Spektor; Andrew M. McNutt; George Grass; Joel S. Freundlich; Robert C. Reynolds; Sean Ekins (J. Chem. Inf. Model., 2015)

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. - Alex M. Clark; Sean Ekins (J. Chem. Inf. Model., 2015)

A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of Mycobacterium tuberculosis InhA. - Alexander L. Perryman; Weixuan Yu; Xin Wang 067; Sean Ekins; Stefano Forli; Shao-Gang Li; Joel S. Freundlich; Peter J. Tonge; Arthur J. Olson (J. Chem. Inf. Model., 2015)

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses. - Alex M. Clark; Krishna Dole; Sean Ekins (J. Chem. Inf. Model., 2016)

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015). - Sean Ekins; Alexander L. Perryman; Alex M. Clark; Robert C. Reynolds; Joel S. Freundlich (J. Chem. Inf. Model., 2016)

The new alchemy: Online networking, data sharing and research activity distribution tools for scientists. - Antony J. Williams; Lou Peck; Sean Ekins (F1000Research, 2017)

Quantum Machine Learning Algorithms for Drug Discovery Applications. - Kushal Batra; Kimberley M. Zorn; Daniel H. Foil; Eni Minerali; Victor O. Gawriljuk; Thomas R. Lane; Sean Ekins (J. Chem. Inf. Model., 2021)

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus. - Victor O. Gawriljuk; Daniel H. Foil; Ana C. Puhl; Kimberley M. Zorn; Thomas R. Lane; Olga Riabova; Vadim Makarov; Andre S. Godoy; Glaucius Oliva; Sean Ekins (J. Chem. Inf. Model., 2021)

Machine Learning Models Identify Inhibitors of SARS-CoV-2. - Victor O. Gawriljuk; Phyo Phyo Kyaw Zin; Ana C. Puhl; Kimberley M. Zorn; Daniel H. Foil; Thomas R. Lane; Brett Hurst; Tatyana Almeida Tavella; Fabio Trindade Maranhão Costa; Premkumar Lakshmanane; Jean Bernatchez; Andre S. Godoy; et al. (J. Chem. Inf. Model., 2021)

Defending Antiviral Cationic Amphiphilic Drugs That May Cause Drug-Induced Phospholipidosis. - Thomas R. Lane; Sean Ekins (J. Chem. Inf. Model., 2021)

Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika. - Melina Mottin; Bruna Katiele de Paula Sousa; Nathalya Cristina de Moraes Roso Mesquita; Ketllyn Irene Zagato de Oliveira; Gabriela Dias Noske; Geraldo Rodrigues Sartori; Aline Albuquerque; Fabio Urbina; Ana C. Puhl; José Teófilo Moreira-Filho; Guilherme E. Souza; Rafael V. C. Guido; et al. (J. Chem. Inf. Model., 2022)

Dual use of artificial-intelligence-powered drug discovery. - Fabio Urbina; Filippa Lentzos; Cédric Invernizzi; Sean Ekins (Nat. Mach. Intell., 2022)

A teachable moment for dual-use. - Fabio Urbina; Filippa Lentzos; Cédric Invernizzi; Sean Ekins (Nat. Mach. Intell., 2022)

Preventing AI From Creating Biochemical Threats. - Fabio Urbina; Filippa Lentzos; Cédric Invernizzi; Sean Ekins (J. Chem. Inf. Model., 2023)

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery. - Matthew A. Dorsey; Kelvin Dsouza; Dhruv Ranganath; Joshua S. Harris; Thomas R. Lane; Fabio Urbina; Sean Ekins (J. Chem. Inf. Model., 2024)

Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors. - Mustafa Kemal Ozalp; Patricia A. Vignaux; Ana C. Puhl; Thomas R. Lane; Fabio Urbina; Sean Ekins (J. Chem. Inf. Model., 2024)