Daniel Reker


Visiting Assistant Professor in the Department 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

  • Visiting Assistant Professor in the Department 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

  • Li, L; Koh, CC; Reker, D; Brown, JB; Wang, H; Lee, NK; Liow, H-H; Dai, H; Fan, H-M; Chen, L; Wei, D-Q, Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees., Scientific Reports, vol 9 no. 1 (2019) [10.1038/s41598-019-43125-6] [abs].
  • Reker, D; Bernardes, GJL; Rodrigues, T, Computational advances in combating colloidal aggregation in drug discovery., Nature Chemistry, vol 11 no. 5 (2019), pp. 402-418 [10.1038/s41557-019-0234-9] [abs].
  • Reker, D; Blum, SM; Steiger, C; Anger, KE; Sommer, JM; Fanikos, J; Traverso, G, "Inactive" ingredients in oral medications., Science Translational Medicine, vol 11 no. 483 (2019) [10.1126/scitranslmed.aau6753] [abs].
  • Reker, D; Brown, JB, Selection of Informative Examples in Chemogenomic Datasets., vol 1825 (2018), pp. 369-410 [10.1007/978-1-4939-8639-2_13] [abs].
  • Reker, D; Schneider, P; Schneider, G; Brown, JB, Active learning for computational chemogenomics., Future Medicinal Chemistry, vol 9 no. 4 (2017), pp. 381-402 [10.4155/fmc-2016-0197] [abs].