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

• Email Address: daniel.reker@duke.edu
• Websites:
  • Google Scholar
  • Reker Lab

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 791: Graduate Independent Study
• BME 792: Continuation of Graduate Independent Study
• EGR 393: Research Projects in Engineering

In the News

• Five Questions for Dan Reker on Bioengineering Better Drug Delivery (Aug 19, 2022 | Duke Government Relations)

Representative Publications

• Fralish, Z; Chen, A; Skaluba, P; Reker, D, DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning (2023) [10.26434/chemrxiv-2023-gbchq] [abs].
• Xiang, Y; Tang, Y-H; Lin, G; Reker, D, Interpretable Molecular Property Predictions Using Marginalized Graph Kernels (2023) [10.26434/chemrxiv-2023-gd1gl] [abs].
• Wen, Y; Li, Z; Xiang, Y; Reker, D, Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning (2023) [10.26434/chemrxiv-2023-h8905] [abs].
• Abramson, A; Kirtane, AR; Shi, Y; Zhong, G; Collins, JE; Tamang, S; Ishida, K; Hayward, A; Wainer, J; Rajesh, NU; Lu, X; Gao, Y; Karandikar, P; Tang, C; Lopes, A; Wahane, A; Reker, D; Frederiksen, MR; Jensen, B; Langer, R; Traverso, G, Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections, Matter, vol 5 no. 3 (2022), pp. 975-987 [10.1016/j.matt.2021.12.022] [abs].
• Shi, Y; Lin, C-H; Reker, D; Steiger, C; Hess, K; Collins, J; Tamang, S; Ishida, K; Lopes, A; Wainer, J; Hayward, A; Walesky, C; Goessling, W; Traverso, G, A machine learning liver-on-a-chip system for safer drug formulation (2022) [10.1101/2022.09.05.506668] [abs].