Daniel Reker
Assistant Professor of Biomedical Engineering
The Reker lab tightly integrates biomedical data science and wet-lab experiments for the analysis and design of therapeutic opportunities. Automated experimentation can be guided by active machine learning to generate knowledge-rich datasets. A key aspect of our research is improving our understanding of the most effective active machine learning workflows to enable the broad deployment of adaptive machine learning and automated experimentation.
We focus our adaptive model development on critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side-effects. Prospective applications of these predictions enable us to better understand limitations of currently approved medications as well as design new drug candidates, nanoparticles, and pharmaceutical formulations. By integrating clinical data analysis, we can rapidly validate the translational relevance of our predictions and conceive big data-driven protocols for precision medicine and personalized drug delivery.
Current Research Interests
Integration of active machine learning, biomedical data science, and biochemical experiments for the analysis and design of personalized therapeutic opportunities.
Current Appointments & Affiliations
- Assistant Professor of Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2021
- Member of the Duke Cancer Institute, Duke Cancer Institute, Institutes and Centers 2022
Contact Information
- Background
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Education, Training, & Certifications
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Previous Appointments & Affiliations
- Visiting Assistant Professor in the Department of Biomedical Engineering, Biomedical Engineering, Pratt School of Engineering 2020
- Recognition
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In the News
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AUG 19, 2022 Duke Government Relations
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- Expertise
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Subject Headings
- Research
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Selected Grants
- NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) awarded by National Science Foundation 2022 - 2027
- University Training Program in Biomolecular and Tissue Engineering awarded by National Institutes of Health 1994 - 2027
- Computational Design of Antibody-Drug-Excipient Nanoparticles awarded by National Institutes of Health 2023 - 2026
- Predicting the effects of microbial metabolites on NOD¿like receptors awarded by University of North Carolina - Chapel Hill 2021 - 2022
- Identifying the molecular mediators of microbiome-host interactions through machine learning awarded by North Carolina Biotechnology Center 2021 - 2022
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External Relationships
- Areteia Therapeutics
- F1000 Research, Chemical Information Gateway
- Frontiers In Drug Discovery
- German Accelerator Life Sciences
- Guidepoint Global
- JSR Corporation
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Abramson, A., A. R. Kirtane, Y. Shi, G. Zhong, J. E. Collins, S. Tamang, K. Ishida, et al. “Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections.” Matter 5, no. 3 (March 2, 2022): 975–87. https://doi.org/10.1016/j.matt.2021.12.022.Full Text
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Steiger, Christoph, Nhi V. Phan, Hen-Wei Huang, Haoying Sun, Jacqueline N. Chu, Daniel Reker, Declan Gwynne, et al. “Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors.” Advanced Science (Weinheim, Baden Wurttemberg, Germany) 8, no. 24 (December 2021): e2102861. https://doi.org/10.1002/advs.202102861.Full Text
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Wollborn, Jakob, Lars O. Hassenzahl, Daniel Reker, Hans Felix Staehle, Anne Marie Omlor, Wolfgang Baar, Kai B. Kaufmann, et al. “Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection.” Annals of Intensive Care 11, no. 1 (December 2021): 175. https://doi.org/10.1186/s13613-021-00965-8.Full Text
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Lee, K., A. Yang, Y. C. Lin, D. Reker, G. J. L. Bernardes, and T. Rodrigues. “Combating small-molecule aggregation with machine learning.” Cell Reports Physical Science 2, no. 9 (September 22, 2021). https://doi.org/10.1016/j.xcrp.2021.100573.Full Text
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Reker, Daniel, Yulia Rybakova, Ameya R. Kirtane, Ruonan Cao, Jee Won Yang, Natsuda Navamajiti, Apolonia Gardner, et al. “Computationally guided high-throughput design of self-assembling drug nanoparticles.” Nature Nanotechnology 16, no. 6 (June 2021): 725–33. https://doi.org/10.1038/s41565-021-00870-y.Full Text
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Reker, D., E. A. Hoyt, G. J. L. Bernardes, and T. Rodrigues. “Adaptive Optimization of Chemical Reactions with Minimal Experimental Information.” Cell Reports Physical Science 1, no. 11 (November 18, 2020). https://doi.org/10.1016/j.xcrp.2020.100247.Full Text
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Reker, Daniel, Steven M. Blum, Peter Wade, Christoph Steiger, and Giovanni Traverso. “Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications.” Pharmaceutical Research 37, no. 12 (October 2020): 234. https://doi.org/10.1007/s11095-020-02953-2.Full Text
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Brown, Nathan, Peter Ertl, Richard Lewis, Torsten Luksch, Daniel Reker, and Nadine Schneider. “Artificial intelligence in chemistry and drug design.” Journal of Computer Aided Molecular Design 34, no. 7 (July 2020): 709–15. https://doi.org/10.1007/s10822-020-00317-x.Full Text
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Erlach, Thomas von, Sarah Saxton, Yunhua Shi, Daniel Minahan, Daniel Reker, Farhad Javid, Young-Ah Lucy Lee, et al. “Robotically handled whole-tissue culture system for the screening of oral drug formulations.” Nature Biomedical Engineering 4, no. 5 (May 2020): 544–59. https://doi.org/10.1038/s41551-020-0545-6.Full Text
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Reker, Daniel, Yunhua Shi, Ameya R. Kirtane, Kaitlyn Hess, Grace J. Zhong, Evan Crane, Chih-Hsin Lin, Robert Langer, and Giovanni Traverso. “Machine Learning Uncovers Food- and Excipient-Drug Interactions.” Cell Reports 30, no. 11 (March 2020): 3710-3716.e4. https://doi.org/10.1016/j.celrep.2020.02.094.Full Text
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Reker, Daniel. “Practical considerations for active machine learning in drug discovery.” Drug Discovery Today. Technologies 32–33 (December 2019): 73–79. https://doi.org/10.1016/j.ddtec.2020.06.001.Full Text
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Reker, Daniel, and Richard A. Lewis. “Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning.” Journal of Computer Aided Molecular Design 33, no. 11 (November 2019): 941. https://doi.org/10.1007/s10822-019-00264-2.Full Text
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Li, Li, Ching Chiek Koh, Daniel Reker, J. B. Brown, Haishuai Wang, Nicholas Keone Lee, Hien-Haw Liow, et al. “Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.” Scientific Reports 9, no. 1 (May 2019): 7703. https://doi.org/10.1038/s41598-019-43125-6.Full Text
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Reker, Daniel, Gonçalo J. L. Bernardes, and Tiago Rodrigues. “Computational advances in combating colloidal aggregation in drug discovery.” Nature Chemistry 11, no. 5 (May 2019): 402–18. https://doi.org/10.1038/s41557-019-0234-9.Full Text
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Reker, Daniel, Steven M. Blum, Christoph Steiger, Kevin E. Anger, Jamie M. Sommer, John Fanikos, and Giovanni Traverso. “"Inactive" ingredients in oral medications.” Science Translational Medicine 11, no. 483 (March 2019): eaau6753. https://doi.org/10.1126/scitranslmed.aau6753.Full Text
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Reker, Daniel, Yulia Rybakova, Ameya Kirtane, Ruonan Cao, Jee Won Yang, Natsuda Navamajiti, Apolonia Gardner, et al. “Computationally guided high-throughput design of self-assembling drug nanoparticles,” 2019. https://doi.org/10.1101/786251.Full Text
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Reker, Daniel, Petra Schneider, Gisbert Schneider, and J. B. Brown. “Active learning for computational chemogenomics.” Future Medicinal Chemistry 9, no. 4 (March 2017): 381–402. https://doi.org/10.4155/fmc-2016-0197.Full Text
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Cui, Jihong, Maija Hollmén, Lina Li, Yong Chen, Steven T. Proulx, Daniel Reker, Gisbert Schneider, and Michael Detmar. “New use of an old drug: inhibition of breast cancer stem cells by benztropine mesylate.” Oncotarget 8, no. 1 (January 2017): 1007–22. https://doi.org/10.18632/oncotarget.13537.Full Text
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Grisoni, Francesca, Daniel Reker, Petra Schneider, Lukas Friedrich, Viviana Consonni, Roberto Todeschini, Andreas Koeberle, Oliver Werz, and Gisbert Schneider. “Matrix-based Molecular Descriptors for Prospective Virtual Compound Screening.” Molecular Informatics 36, no. 1–2 (January 2017). https://doi.org/10.1002/minf.201600091.Full Text
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Schneider, Gisbert, Daniel Reker, Tao Chen, Kurt Hauenstein, Petra Schneider, and Karl-Heinz Altmann. “Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide.” Angewandte Chemie (International Ed. in English) 55, no. 40 (September 2016): 12408–11. https://doi.org/10.1002/anie.201605707.Full Text
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Reker, D., P. Schneider, and G. Schneider. “Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors.” Chemical Science 7, no. 6 (June 2016): 3919–27. https://doi.org/10.1039/c5sc04272k.Full Text
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Rodrigues, Tiago, Daniel Reker, Petra Schneider, and Gisbert Schneider. “Counting on natural products for drug design.” Nature Chemistry 8, no. 6 (June 2016): 531–41. https://doi.org/10.1038/nchem.2479.Full Text
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Schneider, P., M. Röthlisberger, D. Reker, and G. Schneider. “Spotting and designing promiscuous ligands for drug discovery.” Chemical Communications (Cambridge, England) 52, no. 6 (January 2016): 1135–38. https://doi.org/10.1039/c5cc07506h.Full Text
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Rodrigues, Tiago, Daniel Reker, Martin Welin, Michael Caldera, Cyrill Brunner, Gisela Gabernet, Petra Schneider, Björn Walse, and Gisbert Schneider. “De Novo Fragment Design for Drug Discovery and Chemical Biology.” Angewandte Chemie (International Ed. in English) 54, no. 50 (December 2015): 15079–83. https://doi.org/10.1002/anie.201508055.Full Text
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Rodrigues, Tiago, Daniel Reker, Jens Kunze, Petra Schneider, and Gisbert Schneider. “Revealing the Macromolecular Targets of Fragment-Like Natural Products.” Angewandte Chemie (International Ed. in English) 54, no. 36 (September 2015): 10516–20. https://doi.org/10.1002/anie.201504241.Full Text
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Perna, Anna M., Tiago Rodrigues, Thomas P. Schmidt, Manja Böhm, Katharina Stutz, Daniel Reker, Bernhard Pfeiffer, et al. “Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor of Helicobacter Pylori HtrA.” Angewandte Chemie (International Ed. in English) 54, no. 35 (August 2015): 10244–48. https://doi.org/10.1002/anie.201504035.Full Text
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Miyao, Tomoyuki, Daniel Reker, Petra Schneider, Kimito Funatsu, and Gisbert Schneider. “Chemography of natural product space.” Planta Medica 81, no. 6 (April 2015): 429–35. https://doi.org/10.1055/s-0034-1396322.Full Text
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Reker, Daniel, and Gisbert Schneider. “Active-learning strategies in computer-assisted drug discovery.” Drug Discovery Today 20, no. 4 (April 2015): 458–65. https://doi.org/10.1016/j.drudis.2014.12.004.Full Text
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Rodrigues, Tiago, Nadine Hauser, Daniel Reker, Michael Reutlinger, Tiffany Wunderlin, Jacques Hamon, Guido Koch, and Gisbert Schneider. “Multidimensional de novo design reveals 5-HT2B receptor-selective ligands.” Angewandte Chemie (International Ed. in English) 54, no. 5 (January 2015): 1551–55. https://doi.org/10.1002/anie.201410201.Full Text
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Reker, Daniel, Anna M. Perna, Tiago Rodrigues, Petra Schneider, Michael Reutlinger, Bettina Mönch, Andreas Koeberle, et al. “Revealing the macromolecular targets of complex natural products.” Nature Chemistry 6, no. 12 (December 2014): 1072–78. https://doi.org/10.1038/nchem.2095.Full Text
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Schneider, Gisbert, Daniel Reker, Tiago Rodrigues, and Petra Schneider. “Coping with polypharmacology by computational medicinal chemistry.” Chimia 68, no. 9 (September 2014): 648–53. https://doi.org/10.2533/chimia.2014.648.Full Text
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Reker, Daniel, Michael Seet, Max Pillong, Christian P. Koch, Petra Schneider, Matthias C. Witschel, Matthias Rottmann, et al. “Deorphaning pyrrolopyrazines as potent multi-target antimalarial agents.” Angewandte Chemie (International Ed. in English) 53, no. 27 (July 2014): 7079–84. https://doi.org/10.1002/anie.201311162.Full Text
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Reker, Daniel, Tiago Rodrigues, Petra Schneider, and Gisbert Schneider. “Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.” Proceedings of the National Academy of Sciences of the United States of America 111, no. 11 (March 2014): 4067–72. https://doi.org/10.1073/pnas.1320001111.Full Text
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Lötsch, Jörn, Gisbert Schneider, Daniel Reker, Michael J. Parnham, Petra Schneider, Gerd Geisslinger, and Alexandra Doehring. “Common non-epigenetic drugs as epigenetic modulators.” Trends in Molecular Medicine 19, no. 12 (December 2013): 742–53. https://doi.org/10.1016/j.molmed.2013.08.006.Full Text
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Rodrigues, T., F. Roudnicky, C. P. Koch, T. Kudoh, D. Reker, M. Detmar, and G. Schneider. “De novo design and optimization of Aurora A kinase inhibitors.” Chemical Science 4, no. 3 (March 1, 2013): 1229–33. https://doi.org/10.1039/c2sc21842a.Full Text
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Reutlinger, Michael, Christian P. Koch, Daniel Reker, Nickolay Todoroff, Petra Schneider, Tiago Rodrigues, and Gisbert Schneider. “Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules.” Molecular Informatics 32, no. 2 (February 2013): 133–38. https://doi.org/10.1002/minf.201200141.Full Text
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Reker, Daniel, and Lars Malmström. “Bioinformatic challenges in targeted proteomics.” Journal of Proteome Research 11, no. 9 (September 2012): 4393–4402. https://doi.org/10.1021/pr300276f.Full Text
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Reker, Daniel, Stefan Katzenbeisser, and Kay Hamacher. “Computation of mutual information from Hidden Markov Models.” Computational Biology and Chemistry 34, no. 5–6 (December 2010): 328–33. https://doi.org/10.1016/j.compbiolchem.2010.08.005.Full Text
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Book Sections
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Reker, D. “Chapter 14: Active Learning for Drug Discovery and Automated Data Curation.” In RSC Drug Discovery Series, 2021-January:301–26, 2021. https://doi.org/10.1039/9781788016841-00301.Full Text
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Reker, Daniel. “Cheminformatic Analysis of Natural Product Fragments.,” 110:143–75, 2019. https://doi.org/10.1007/978-3-030-14632-0_5.Full Text
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Reker, Daniel, and J. B. Brown. “Selection of Informative Examples in Chemogenomic Datasets.,” 1825:369–410, 2018. https://doi.org/10.1007/978-1-4939-8639-2_13.Full Text
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Preprints
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Fralish, Zachary, Ashley Chen, Paul Skaluba, and Daniel Reker. “DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning.” American Chemical Society (ACS), April 11, 2023. https://doi.org/10.26434/chemrxiv-2023-gbchq.Full Text
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Xiang, Yan, Yu-Hang Tang, Guang Lin, and Daniel Reker. “Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.” American Chemical Society (ACS), February 20, 2023. https://doi.org/10.26434/chemrxiv-2023-gd1gl.Full Text
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Wen, Yujing, Zhixiong Li, Yan Xiang, and Daniel Reker. “Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning.” American Chemical Society (ACS), February 13, 2023. https://doi.org/10.26434/chemrxiv-2023-h8905.Full Text
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Shi, Yunhua, Chih-Hsin Lin, Daniel Reker, Christoph Steiger, Kaitlyn Hess, Joy Collins, Siddartha Tamang, et al. “A machine learning liver-on-a-chip system for safer drug formulation.” BioRxiv, 2022. https://doi.org/10.1101/2022.09.05.506668.Full Text
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- Teaching & Mentoring
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Recent Courses
- BME 221L: Biomaterials 2023
- BME 394: Projects in Biomedical Engineering (GE) 2023
- BME 493: Projects in Biomedical Engineering (GE) 2023
- BME 494: Projects in Biomedical Engineering (GE) 2023
- BME 590L: Special Topics with Lab 2023
- BME 221L: Biomaterials 2022
- BME 493: Projects in Biomedical Engineering (GE) 2022
- BME 590L: Special Topics with Lab 2022
- BME 791: Graduate Independent Study 2022
- EGR 393: Research Projects in Engineering 2022
- BME 590L: Special Topics with Lab 2021
- BME 791: Graduate Independent Study 2021
- BME 792: Continuation of Graduate Independent Study 2021
- Scholarly, Clinical, & Service Activities
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Service to the Profession
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