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

Contact Information


  • 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 791: Graduate Independent Study

Representative Publications

  • Reker, D; Blum, SM; Wade, P; Steiger, C; Traverso, G, Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications., Pharm Res, vol 37 no. 12 (2020) [10.1007/s11095-020-02953-2] [abs].
  • Brown, N; Ertl, P; Lewis, R; Luksch, T; Reker, D; Schneider, N, Artificial intelligence in chemistry and drug design., Journal of Computer Aided Molecular Design, vol 34 no. 7 (2020), pp. 709-715 [10.1007/s10822-020-00317-x] [abs].
  • von Erlach, T; Saxton, S; Shi, Y; Minahan, D; Reker, D; Javid, F; Lee, Y-AL; Schoellhammer, C; Esfandiary, T; Cleveland, C; Booth, L; Lin, J; Levy, H; Blackburn, S; Hayward, A; Langer, R; Traverso, G, Robotically handled whole-tissue culture system for the screening of oral drug formulations., Nature Biomedical Engineering, vol 4 no. 5 (2020), pp. 544-559 [10.1038/s41551-020-0545-6] [abs].
  • Reker, D; Shi, Y; Kirtane, AR; Hess, K; Zhong, GJ; Crane, E; Lin, C-H; Langer, R; Traverso, G, Machine Learning Uncovers Food- and Excipient-Drug Interactions., Cell Reports, vol 30 no. 11 (2020), pp. 3710-3716.e4 [10.1016/j.celrep.2020.02.094] [abs].
  • Reker, D, Practical considerations for active machine learning in drug discovery., Drug Discovery Today: Technologies, vol 32-33 (2019), pp. 73-79 [10.1016/j.ddtec.2020.06.001] [abs].