Ekins, Sean

Exploiting machine learning for end-to-end drug discovery and development

Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 578

Dual use of artificial-intelligence-powered drug discovery

Urbina, F., Lentzos, F., Invernizzi, C., & Ekins, S. (2022) · Nature Machine Intelligence

Computational Drug Discovery MethodsLaw, AI, and Intellectual Property

Cited by 346

Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction

Krasowski, M. D., Drees, D., Morris, C. S., Maakestad, J., Blau, J. L., & Ekins, S. (2014) · BMC Clinical Pathology

Clinical Laboratory Practices and Quality ControlAdrenal Hormones and Disorders

Cited by 212

A critical overview of computational approaches employed for COVID-19 drug discovery

Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews

Computational Drug Discovery MethodsSARS-CoV-2 and COVID-19 Research

Cited by 206

Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 152

Drug transporters on arachnoid barrier cells contribute to the blood-cerebrospinal fluid barrier

Yasuda, K., Cline, C., Vogel, P., Onciu, M., Fatima, S., Sorrentino, B. P., Thirumaran, R. K., Ekins, S., Urade, Y., Fujimori, K., et al. (2013) · Drug Metabolism and Disposition

Drug Transport and Resistance MechanismsBarrier Structure and Function Studies

Cited by 128

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

Clark, A. M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J. S., Reynolds, R. C., & Ekins, S. (2015) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 114

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Ekins, S., Lage de Siqueira-Neto, J., McCall, L.-I., Sarker, M., Yadav, M., Ponder, E. L., Kallel, E. A., Kellar, D., Chen, S., Arkin, M., et al. (2015) · PLOS Neglected Tropical Diseases

Computational Drug Discovery MethodsTrypanosoma species research and implications

Cited by 93

The natural product eugenol is an inhibitor of the ebola virus in vitro

Lane, T., Anantpadma, M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S. (2019) · Pharmaceutical Research

Viral Infections and Outbreaks ResearchMedicinal Plant Research

Cited by 88

Machine learning models identify molecules active against the Ebola virus in vitro

Ekins, S., Freundlich, J. S., Clark, A. M., Anantpadma, M., Davey, R. A., & Madrid, P. (2015) · F1000Research

Computational Drug Discovery MethodsViral Infections and Outbreaks Research

Cited by 84

Ten simple rules of live tweeting at scientific conferences.

Ekins, S., & Perlstein, E. O. (2014) · PLoS Computational Biology

Academic Publishing and Open AccessSocial Media in Health Education

Cited by 55

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

Anantpadma, M., Lane, T., Zorn, K. M., Lingerfelt, M. A., Clark, A. M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S. (2019) · ACS Omega

Computational Drug Discovery MethodsViral Infections and Outbreaks Research

Cited by 54

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

Perryman, A. L., Stratton, T. P., Ekins, S., & Freundlich, J. S. (2016) · Pharmaceutical Research

Computational Drug Discovery MethodsPharmacogenetics and Drug Metabolism

Cited by 50

Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data

Clark, A. M., Williams, A. J., & Ekins, S. (2015) · Journal of Cheminformatics

Research Data Management PracticesScientific Computing and Data Management

Cited by 33

Naïve Bayesian Models for Vero Cell Cytotoxicity

Perryman, A. L., Patel, J. S., Russo, R., Singleton, E., Connell, N., Ekins, S., & Freundlich, J. S. (2018) · Pharmaceutical Research

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 33

Industrializing rare disease therapy discovery and development

Ekins, S. (2017) · Nature Biotechnology

Genomics and Rare DiseasesScience, Research, and Medicine

Cited by 28

Addressing the Metabolic Stability of Antituberculars through Machine Learning

Stratton, T. P., Perryman, A. L., Vilchèze, C., Russo, R., Li, S.-G., Patel, J. S., Singleton, E., Ekins, S., Connell, N., Jacobs, W. R., et al. (2017) · ACS Medicinal Chemistry Letters

Computational Drug Discovery MethodsPharmacogenetics and Drug Metabolism

Cited by 14

Doing it All - How Families are Reshaping Rare Disease Research

Ekins, S., & Perlstein, E. O. (2018) · Pharmaceutical Research

Mental Health and Patient InvolvementBiomedical Ethics and Regulation

Cited by 10