Assistant Professor
Department of Biomedical Engineering

Duke Science and Technology Fellow

Duke University, Durham, NC 27708

United States

Group Website: rekerlab.pratt.duke.edu

Daniel Reker is an Assistant Professor in the Department of Biomedical Engineering at Duke University, a Duke Science and Technology Fellow, and a member of the Duke Cancer Institute. Trained in computer science and computational biology, he earned his doctorate in pharmaceutical sciences at ETH Zurich with Gisbert Schneider and completed postdoctoral research in drug delivery at MIT with Robert Langer and Giovanni Traverso as a Swiss National Science Foundation Fellow. His laboratory integrates machine learning, molecular simulation, and automated experimentation to accelerate the design of drugs and drug delivery solutions. His team develops self-assembling drug–excipient nanoparticles and computational approaches to select their components and optimize formulation ratios, with experimental evaluation of drug loading, stability, and biological performance. Recent contributions include TuNa-AI for nanoparticle composition optimization, dye-based formulations with enhanced lung accumulation in preclinical studies, and physics-informed yoked deep learning for discovering new drug–excipient combinations. His group also applies computational approaches to inorganic nanoparticles and is extending its formulation platforms to lipid nanoparticles (LNPs). Complementing these nanoparticle efforts, his group develops integrated computational workflows for small-molecule prodrug design, with the goal of improving efficacy and reducing adverse effects relative to the parent drug. Daniel has been recognized as Forbes 30 Under 30 Europe in Science and Healthcare and he has been awarded the NIH NIBIB Trailblazer Award, an NIH NIGMS Outstanding Investigator Award, and Duke's Klein Family Distinguished Teaching Award.

Representative Publications

[1] Z. Zhang, Y. Xiang, J. Laforet, I. Spasojevic, P. Fan, A. Heffernan, C.E. Eyler, K.C. Wood, Z.C. Hartman, D. Reker. TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery. ACS Nano 19, 33288–33296 (2025).

[2] L.A. Onweller, R.T. Stiepel, G.A. Cortina, J.R. Laforet Jr., I. Spasojevic, P. Fan, D. Reker. Fine-tuned machine learning models for the discovery of dye nanoparticles with enhanced lung delivery. J Control Release 398, 115226 (2026).

[3] Y. Xiang, Z. Zhang, R.T. Stiepel, C.S. Potnis, L.A. Onweller, J.R. Laforet Jr., H. Gowda, D. Reker. Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning. npj Comput Mater (2026), advance online publication. doi:10.1038/s41524-026-02262-z.

[4] B.B. Mendes, Z. Zhang, J. Conniot, D.P. Sousa, J.M.J.M. Ravasco, L.A. Onweller, A. Lorenc, T. Rodrigues, D. Reker, J. Conde. A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research. Nat Nanotechnol 19, 867–878 (2024).

[5] Z. Fralish, A. Chen, S. Khan, P. Zhou, D. Reker. The landscape of small-molecule prodrugs. Nat Rev Drug Discov 23, 365–380 (2024).