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.

Appointments and Affiliations

  • Assistant Professor of Biomedical Engineering
  • Member of the Duke Cancer Institute

Contact Information

Education

  • Sc.D. Swiss Federal Institute of Technology-ETH Zurich (Switzerland), 2016

Research Interests

Integration of active machine learning, biomedical data science, and biochemical experiments for the analysis and design of personalized therapeutic opportunities.

Courses Taught

  • EGR 393: Research Projects in Engineering
  • BME 793: Graduate Independent Study
  • BME 792: Continuation of Graduate Independent Study
  • BME 791: Graduate Independent Study
  • BME 790L: Advanced Topics with the Lab for Graduate Students in Biomedical Engineering
  • BME 789: Internship in Biomedical Engineering
  • BME 713S: QBio Seminar Series
  • BME 590L: Special Topics with Lab
  • BME 494: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 390L: Special Topics with a Lab

In the News

Representative Publications

  • Markey, Chloe, Zachary Fralish, Hannah Lee, and Daniel Reker. “Ensemble Siamese Neural Networks for Prodrug Activation Prediction.” American Chemical Society (ACS), November 27, 2025. https://doi.org/10.26434/chemrxiv-2025-pn8qg.
  • Kim, Sarah, Taranpreet Kaur, Yulia Shmidov, Matthew Wang, Lixin Fan, Abigail Leo, Yan Xiang, Daniel Reker, and Ashutosh Chilkoti. “Genetically encoded sterol-modification of a synthetic intrinsically disordered protein leads to diverse self-assembly behavior.” American Chemical Society (ACS), October 15, 2025. https://doi.org/10.26434/chemrxiv-2025-5zcxx.
  • Zhang, Zilu, Yan Xiang, Joe Laforet, Ivan Spasojevic, Ping Fan, Ava Heffernan, Christine E. Eyler, Kris C. Wood, Zachary C. Hartman, and Daniel Reker. “TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery.” ACS Nano 19, no. 37 (September 23, 2025): 33288–96. https://doi.org/10.1021/acsnano.5c09066.
  • Chung, Hong A., Zachary Fralish, Tiffany Tu, and Daniel Reker. “Profiling Biological Effects of Microbiome Metabolites via Machine Learning.” American Chemical Society (ACS), August 4, 2025. https://doi.org/10.26434/chemrxiv-2025-15mw9.
  • Gowda, Hrshita, Wenbo Lu, Paul Skaluba, Yan Xiang, Jessica McCann, Laura McCoubrey, John Rawls, Ophelia Venturelli, and Daniel Reker. “Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.” American Chemical Society (ACS), June 11, 2025. https://doi.org/10.26434/chemrxiv-2025-lddpg.