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 792: Continuation of Graduate Independent Study
  • BME 791: Graduate Independent Study
  • 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 394: Projects in Biomedical Engineering (GE)
  • BME 221L: Biomaterials

In the News

Representative Publications

  • Li, Z., Y. Xiang, Y. Wen, and D. Reker. “Yoked learning in molecular data science.” Artificial Intelligence in the Life Sciences 5 (June 1, 2024). https://doi.org/10.1016/j.ailsci.2023.100089.
  • Mendes, Bárbara B., Zilu Zhang, João Conniot, Diana P. Sousa, João M. J. M. Ravasco, Lauren A. Onweller, Andżelika Lorenc, Tiago Rodrigues, Daniel Reker, and João Conde. “A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.” Nature Nanotechnology 19, no. 6 (June 2024): 867–78. https://doi.org/10.1038/s41565-024-01673-7.
  • Fralish, Zachary, Ashley Chen, Shaharyar Khan, Pei Zhou, and Daniel Reker. “The landscape of small-molecule prodrugs.” Nat Rev Drug Discov 23, no. 5 (May 2024): 365–80. https://doi.org/10.1038/s41573-024-00914-7.
  • Navamajiti, Natsuda, Apolonia Gardner, Ruonan Cao, Yutaro Sugimoto, Jee Won Yang, Aaron Lopes, Nhi V. Phan, et al. “Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery.” Journal of Pharmaceutical Sciences 113, no. 3 (March 2024): 718–24. https://doi.org/10.1016/j.xphs.2023.09.001.
  • Shi, Yunhua, Daniel Reker, James D. Byrne, Ameya R. Kirtane, Kaitlyn Hess, Zhuyi Wang, Natsuda Navamajiti, et al. “Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.” Nature Biomedical Engineering 8, no. 3 (March 2024): 278–90. https://doi.org/10.1038/s41551-023-01128-9.