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

  • BME 221L: Biomaterials
  • BME 394: Projects in Biomedical Engineering (GE)
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 494: Projects in Biomedical Engineering (GE)
  • BME 590L: Special Topics with Lab
  • BME 713S: QBio Seminar Series
  • BME 791: Graduate Independent Study
  • EGR 393: Research Projects in Engineering

In the News

Representative Publications

  • Li, Z., Y. Xiang, Y. Wen, and D. Reker. “Yoked learning in molecular data science (Accepted).” Artificial Intelligence in the Life Sciences 5 (June 1, 2024). https://doi.org/10.1016/j.ailsci.2023.100089.
  • Mullowney, Michael W., Katherine R. Duncan, Somayah S. Elsayed, Neha Garg, Justin J. J. van der Hooft, Nathaniel I. Martin, David Meijer, et al. “Artificial intelligence for natural product drug discovery.” Nature Reviews. Drug Discovery 22, no. 11 (November 2023): 895–916. https://doi.org/10.1038/s41573-023-00774-7.
  • Fralish, Zachary, Ashley Chen, Paul Skaluba, and Daniel Reker. “DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.” Journal of Cheminformatics 15, no. 1 (October 2023): 101. https://doi.org/10.1186/s13321-023-00769-x.
  • 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, September 2023, S0022-3549(23)00364-7. https://doi.org/10.1016/j.xphs.2023.09.001.
  • Li, Zhixiong, Yan Xiang, Yujing Wen, and Daniel Reker. “Yoked Learning in Molecular Data Science.” American Chemical Society (ACS), August 16, 2023. https://doi.org/10.26434/chemrxiv-2023-80fd7.