Institution · Collaborations Pharmaceuticals (United States)
Most cited papers from Collaborations Pharmaceuticals (United States)
What are the most cited papers from Collaborations Pharmaceuticals (United States)?
The most cited paper from Collaborations Pharmaceuticals (United States) is "Exploiting machine learning for end-to-end drug discovery and development" (Sean Ekins, Ana C. Puhl, Kimberley M. Zorn et al., 2019), with 606 citations. Next come "Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets" (368) and "Dual use of artificial-intelligence-powered drug discovery" (343).
Showing 50 of 60 papers. Every paper with more than 60 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Exploiting machine learning for end-to-end drug discovery and development Sean Ekins, Ana C. Puhl, Kimberley M. Zorn et al. · Nature Materials | 18 Apr 2019 | 606 |
| 2 | Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets Alexandru V. Korotcov, Valery Tkachenko, Daniel P. Russo et al. · Molecular Pharmaceutics | 3 Nov 2017 | 368 |
| 3 | Dual use of artificial-intelligence-powered drug discovery Fabio Urbina, Filippa Lentzos, Cédric F. Invernizzi et al. · Nature Machine Intelligence | 7 Mar 2022 | 343 |
| 4 | In silico repositioning of approved drugs for rare and neglected diseases Sean Ekins, Antony John Williams, Matthew David Krasowski et al. · Drug Discovery Today | 7 Mar 2011 | 296 |
| 5 | The Next Era: Deep Learning in Pharmaceutical Research Sean Ekins · Pharmaceutical Research | 6 Sep 2016 | 240 |
| 6 | A bibliometric review of drug repurposing Nancy Baker, Sean Ekins, Antony John Williams et al. · Drug Discovery Today | 9 Jan 2018 | 215 |
| 7 | A critical overview of computational approaches employed for COVID-19 drug discovery Eugene N. Muratov, Rommie Elizabeth Amaro, Carolina Horta Andrade et al. · Chemical Society Reviews | 2021 | 214 |
| 8 | Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction Matthew David Krasowski, Denny Drees, Cory S. Morris et al. · BMC Clinical Pathology | 14 Jul 2014 | 213 |
| 9 | Quantum Machine Learning Algorithms for Drug Discovery Applications Kushal Batra, Kimberley M. Zorn, Daniel H. Foil et al. · Journal of Chemical Information and Modeling | 25 May 2021 | 173 |
| 10 | Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction Daniel P. Russo, Kimberley M. Zorn, Alex M. Clark et al. · Molecular Pharmaceutics | 16 Aug 2018 | 157 |
| 11 | CATMoS: Collaborative Acute Toxicity Modeling Suite Kamel Mansouri et al. · Environmental Health Perspectives | Apr 2021 | 149 |
| 12 | Non-classical transpeptidases yield insight into new antibacterials Pankaj Kumar, Amit Kumar Kaushik, Evan P. Lloyd et al. · Nature Chemical Biology | 2 Nov 2016 | 143 |
| 13 | Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery Moses M. Hohman, Kellan Gregory, Kelly Chibale et al. · Drug Discovery Today | 17 Feb 2009 | 139 |
| 14 | Drug Transporters on Arachnoid Barrier Cells Contribute to the Blood–Cerebrospinal Fluid Barrier Kazuto Yasuda, Cynthia Cline, Peter Vogel et al. · Drug Metabolism and Disposition | 9 Jan 2013 | 131 |
| 15 | Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation Antony John Williams, Sean Ekins, Valery Tkachenko · Drug Discovery Today | 8 Mar 2012 | 131 |
| 16 | A Predictive Ligand-Based Bayesian Model for Human Drug-Induced Liver Injury Sean Ekins, Antony John Williams, Jinghai James Xu · Drug Metabolism and Disposition | 16 Sep 2010 | 125 |
| 17 | Opportunities and challenges using artificial intelligence in ADME/Tox Barun Bhhatarai, W. Patrick Walters, Cornelis E. C. A. Hop et al. · Nature Materials | 18 Apr 2019 | 124 |
| 18 | Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery Thomas R. Lane, Daniel P. Russo, Kimberley M. Zorn et al. · Molecular Pharmaceutics | 19 Apr 2018 | 120 |
| 19 | Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets Alex M. Clark, Krishna Dole, Anna Coulon-Spektor et al. · Journal of Chemical Information and Modeling | 21 May 2015 | 119 |
| 20 | Evolution of promiscuous nuclear hormone receptors: LXR, FXR, VDR, PXR, and CAR Matthew David Krasowski, Ai Ni, Lee R. Hagey et al. · Molecular and Cellular Endocrinology | 7 Jul 2010 | 119 |
| 21 | Repurposing Quaternary Ammonium Compounds as Potential Treatments for COVID-19 Nancy Baker, Antony John Williams, Alexander Tropsha et al. · Pharmaceutical Research | 25 May 2020 | 118 |
| 22 | Evolution of pharmacologic specificity in the pregnane X receptor Sean Ekins, Erica J. Reschly, Lee R. Hagey et al. · BMC Evolutionary Biology | 2 Apr 2008 | 113 |
| 23 | Evolution of the bile salt nuclear receptor FXR in vertebrates* Erica J. Reschly, Ni Ai, Sean Ekins et al. · Journal of Lipid Research | 25 Mar 2008 | 112 |
| 24 | Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI) Eni Minerali, Daniel H. Foil, Kimberley M. Zorn et al. · Molecular Pharmaceutics | 18 May 2020 | 109 |
| 25 | Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR Sean Ekins, Sandhya Kortagere, Manisha Iyer et al. · PLoS Computational Biology | 10 Dec 2009 | 108 |
| 26 | A quality alert and call for improved curation of public chemistry databases Antony John Williams, Sean Ekins · Drug Discovery Today | 3 Aug 2011 | 107 |
| 27 | Progress in computational toxicology Sean Ekins · Journal of Pharmacological and Toxicological Methods | 20 Dec 2013 | 105 |
| 28 | New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules Sandhya Kortagere, Dmitriy S. Chekmarev, William Jeffrey Welsh et al. · Pharmaceutical Research | 15 Apr 2008 | 105 |
| 29 | Déjà vu: Stimulating open drug discovery for SARS-CoV-2 Sean Ekins, Melina Mottin, Paulo R.P.S. Ramos et al. · Drug Discovery Today | 19 Apr 2020 | 104 |
| 30 | Evolving molecules using multi-objective optimization: applying to ADME/Tox Sean Ekins, J. Dana Honeycutt, James T. Metz · Drug Discovery Today | 3 May 2010 | 102 |
| 31 | Structure–Activity Relationship for FDA Approved Drugs As Inhibitors of the Human Sodium Taurocholate Cotransporting Polypeptide (NTCP) Zhongqi Dong, Sean Ekins, James E. Polli · Molecular Pharmaceutics | 22 Jan 2013 | 101 |
| 32 | Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening Liying Zhang, Denis Fourches, Alexander Sedykh et al. · Journal of Chemical Information and Modeling | 19 Dec 2012 | 99 |
| 33 | Assessment of Substrate-Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates Philip J. Sandoval, Kimberley M. Zorn, Alex M. Clark et al. · Molecular Pharmacology | 8 Jun 2018 | 97 |
| 34 | Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery Sean Ekins, Robert C. Reynolds, Hiyun Kim et al. · Chemistry & Biology | Mar 2013 | 97 |
| 35 | Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery Sean Ekins, Jair L. Siqueira-Neto, Laura‐Isobel McCall et al. · PLoS neglected tropical diseases | 26 Jun 2015 | 96 |
| 36 | Machine learning models identify molecules active against the Ebola virus in vitro Sean Ekins, Joel S. Freundlich, Alex M. Clark et al. · F1000Research | 20 Oct 2015 | 88 |
| 37 | The Natural Product Eugenol Is an Inhibitor of the Ebola Virus In Vitro Thomas R. Lane, Manu Anantpadma, Joel S. Freundlich et al. · Pharmaceutical Research | 17 May 2019 | 87 |
| 38 | Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery Sean Ekins, Joel S. Freundlich, Inhee Choi et al. · Trends in Microbiology | 3 Dec 2010 | 85 |
| 39 | A collaborative database and computational models for tuberculosis drug discovery Sean Ekins, Justin Bradford, Krishna Dole et al. · Molecular BioSystems | 9 Feb 2010 | 83 |
| 40 | Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL Alex M. Clark, Sean Ekins · Journal of Chemical Information and Modeling | 21 May 2015 | 79 |
| 41 | Repurposing the Ebola and Marburg Virus Inhibitors Tilorone, Quinacrine, and Pyronaridine: In Vitro Activity against SARS-CoV‑2 and Potential Mechanisms Ana C. Puhl et al. · ACS Omega | 10 Mar 2021 | 78 |
| 42 | Computational Discovery of Novel Low Micromolar Human Pregnane X Receptor Antagonists Sean Ekins, Vladyslav V. Kholodovych, Ni Ai et al. · Molecular Pharmacology | 26 Jun 2008 | 75 |
| 43 | Using molecular similarity to highlight the challenges of routine immunoassay-based drug of abuse/toxicology screening in emergency medicine Matthew David Krasowski, Anthony F. Pizon, Mohamed G Siam et al. · BMC Emergency Medicine | 28 Apr 2009 | 73 |
| 44 | Recent advances in drug repurposing using machine learning Fabio Urbina, Ana C. Puhl, Sean Ekins · Current Opinion in Chemical Biology | 16 Jul 2021 | 72 |
| 45 | Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection Sean Ekins, Mary A. Lingerfelt, Jason E. Comer et al. · Antimicrobial Agents and Chemotherapy | 13 Nov 2017 | 72 |
| 46 | Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis Sean Ekins, Takushi Kaneko, Christopher A. Lipinski et al. · Molecular BioSystems | 8 Sep 2010 | 72 |
| 47 | Applications and Limitations of In Silico Models in Drug Discovery Ahmet Saçan, Sean Ekins, Sandhya Kortagere · Methods in molecular biology | 2012 | 69 |
| 48 | Ligand specificity and evolution of liver X receptors Erica J. Reschly, Ni Ai, William Jeffrey Welsh et al. · The Journal of Steroid Biochemistry and Molecular Biology | 11 Mar 2008 | 69 |
| 49 | Halogenated ligands and their interactions with amino acids: Implications for structure–activity and structure–toxicity relationships Sandhya Kortagere, Sean Ekins, William Jeffrey Welsh · Journal of Molecular Graphics and Modelling | 11 Apr 2008 | 68 |
| 50 | Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets Kimberley M. Zorn, Thomas R. Lane, Daniel P. Russo et al. · Molecular Pharmaceutics | 19 Feb 2019 | 65 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.