Daniel Reker

Research Interests

Integration of active machine learning, biomedical data science, and biochemical experiments for the analysis and design of personalized therapeutic opportunities.

Bio

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.

Education

• Sc.D. Swiss Federal Institute of Technology-ETH Zurich (Switzerland), 2016

Positions

• Assistant Professor of Biomedical Engineering
• Member of the Duke Cancer Institute

Courses Taught

• EGR 393: Research Projects in Engineering
• BME 792: Continuation of Graduate Independent Study
• BME 791: Graduate Independent Study
• BME 713S: QBio Seminar Series
• BME 590L: Special Topics with Lab
• BME 494: Projects in Biomedical Engineering (GE)
• BME 493: Projects in Biomedical Engineering (GE)
• BME 394: Projects in Biomedical Engineering (GE)
• BME 390L: Special Topics with a Lab
• BME 221L: Biomaterials

Publications

• Fralish Z, Reker D. Taking a deep dive with active learning for drug discovery. Nature computational science. 2024 Oct;4(10):727–8.
• Markey CE, Reker D. Machine learning trims the peptide drug design process to a sweet spot. Nature chemistry. 2024 Sep;16(9):1394–5.
• Li Z, Xiang Y, Wen Y, Reker D. Yoked learning in molecular data science. Artificial Intelligence in the Life Sciences. 2024 Jun 1;5.
• Mendes BB, Zhang Z, Conniot J, Sousa DP, Ravasco JMJM, Onweller LA, et al. A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research. Nature nanotechnology. 2024 Jun;19(6):867–78.
• Fralish Z, Chen A, Khan S, Zhou P, Reker D. The landscape of small-molecule prodrugs. Nat Rev Drug Discov. 2024 May;23(5):365–80.
• Shi Y, Reker D, Byrne JD, Kirtane AR, Hess K, Wang Z, et al. Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning. Nature biomedical engineering. 2024 Mar;8(3):278–90.
• Navamajiti N, Gardner A, Cao R, Sugimoto Y, Yang JW, Lopes A, et al. Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery. Journal of pharmaceutical sciences. 2024 Mar;113(3):718–24.
• Markey C, Croset S, Woolley OR, Buldun CM, Koch C, Koller D, et al. Characterizing emerging companies in computational drug development. Nature computational science. 2024 Feb;4(2):96–103.
• Fralish Z, Reker D. Finding the most potent compounds using active learning on molecular pairs. Beilstein journal of organic chemistry. 2024 Jan;20:2152–62.
• Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, Martin NI, et al. Artificial intelligence for natural product drug discovery. Nature reviews Drug discovery. 2023 Nov;22(11):895–916.
• Fralish Z, Chen A, Skaluba P, Reker D. DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. Journal of cheminformatics. 2023 Oct;15(1):101.
• Li Z, Xiang Y, Wen Y, Reker D. Yoked Learning in Molecular Data Science. American Chemical Society (ACS). 2023.
• Wen Y, Li Z, Xiang Y, Reker D. Improving molecular machine learning through adaptive subsampling with active learning. Digital Discovery. 2023 Aug 1;2(4):1134–42.
• Xiang Y, Tang Y-H, Lin G, Reker D. Interpretable Molecular Property Predictions Using Marginalized Graph Kernels. Journal of chemical information and modeling. 2023 Aug;63(15):4633–40.
• Fralish Z, Chen A, Skaluba P, Reker D. DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning. American Chemical Society (ACS). 2023.
• Xiang Y, Tang Y-H, Lin G, Reker D. Interpretable Molecular Property Predictions Using Marginalized Graph Kernels. American Chemical Society (ACS). 2023.
• Wen Y, Li Z, Xiang Y, Reker D. Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning. American Chemical Society (ACS). 2023.
• Abramson A, Kirtane AR, Shi Y, Zhong G, Collins JE, Tamang S, et al. Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections. Matter. 2022 Mar 2;5(3):975–87.
• Shi Y, Lin C-H, Reker D, Steiger C, Hess K, Collins J, et al. A machine learning liver-on-a-chip system for safer drug formulation. bioRxiv. 2022.
• Steiger C, Phan NV, Huang H-W, Sun H, Chu JN, Reker D, et al. Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors. Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2021 Dec;8(24):e2102861.
• Wollborn J, Hassenzahl LO, Reker D, Staehle HF, Omlor AM, Baar W, et al. Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection. Annals of intensive care. 2021 Dec;11(1):175.
• Lee K, Yang A, Lin YC, Reker D, Bernardes GJL, Rodrigues T. Combating small-molecule aggregation with machine learning. Cell Reports Physical Science. 2021 Sep 22;2(9).
• Reker D, Rybakova Y, Kirtane AR, Cao R, Yang JW, Navamajiti N, et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nature nanotechnology. 2021 Jun;16(6):725–33.
• Reker D. Chapter 14: Active Learning for Drug Discovery and Automated Data Curation. In: RSC Drug Discovery Series. 2021. p. 301–26.
• Reker D, Hoyt EA, Bernardes GJL, Rodrigues T. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information. Cell Reports Physical Science. 2020 Nov 18;1(11).
• Reker D, Blum SM, Wade P, Steiger C, Traverso G. Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications. Pharmaceutical research. 2020 Oct;37(12):234.
• 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. 2020 Jul;34(7):709–15.
• von Erlach T, Saxton S, Shi Y, Minahan D, Reker D, Javid F, et al. Robotically handled whole-tissue culture system for the screening of oral drug formulations. Nature biomedical engineering. 2020 May;4(5):544–59.
• Reker D, Shi Y, Kirtane AR, Hess K, Zhong GJ, Crane E, et al. Machine Learning Uncovers Food- and Excipient-Drug Interactions. Cell reports. 2020 Mar;30(11):3710-3716.e4.
• Reker D. Practical considerations for active machine learning in drug discovery. Drug discovery today Technologies. 2019 Dec;32–33:73–9.
• Reker D, Lewis RA. Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning. Journal of computer-aided molecular design. 2019 Nov;33(11):941.
• Reker D, Bernardes GJL, Rodrigues T. Computational advances in combating colloidal aggregation in drug discovery. Nature chemistry. 2019 May;11(5):402–18.
• Li L, Koh CC, Reker D, Brown JB, Wang H, Lee NK, et al. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Scientific reports. 2019 May;9(1):7703.
• Reker D, Blum SM, Steiger C, Anger KE, Sommer JM, Fanikos J, et al. "Inactive" ingredients in oral medications. Science translational medicine. 2019 Mar;11(483):eaau6753.
• Reker D, Rybakova Y, Kirtane A, Cao R, Yang JW, Navamajiti N, et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. 2019;
• Reker D. Cheminformatic Analysis of Natural Product Fragments. In 2019. p. 143–75.
• Reker D, Brown JB. Selection of Informative Examples in Chemogenomic Datasets. In 2018. p. 369–410.
• Reker D, Schneider P, Schneider G, Brown JB. Active learning for computational chemogenomics. Future medicinal chemistry. 2017 Mar;9(4):381–402.
• Cui J, Hollmén M, Li L, Chen Y, Proulx ST, Reker D, et al. New use of an old drug: inhibition of breast cancer stem cells by benztropine mesylate. Oncotarget. 2017 Jan;8(1):1007–22.
• Grisoni F, Reker D, Schneider P, Friedrich L, Consonni V, Todeschini R, et al. Matrix-based Molecular Descriptors for Prospective Virtual Compound Screening. Molecular informatics. 2017 Jan;36(1–2).
• Schneider G, Reker D, Chen T, Hauenstein K, Schneider P, Altmann K-H. Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide. Angewandte Chemie (International ed in English). 2016 Sep;55(40):12408–11.
• Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design. Nature chemistry. 2016 Jun;8(6):531–41.
• Reker D, Schneider P, Schneider G. Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors. Chemical science. 2016 Jun;7(6):3919–27.
• Schneider P, Röthlisberger M, Reker D, Schneider G. Spotting and designing promiscuous ligands for drug discovery. Chemical communications (Cambridge, England). 2016 Jan;52(6):1135–8.
• Rodrigues T, Reker D, Welin M, Caldera M, Brunner C, Gabernet G, et al. De Novo Fragment Design for Drug Discovery and Chemical Biology. Angewandte Chemie (International ed in English). 2015 Dec;54(50):15079–83.
• Rodrigues T, Reker D, Kunze J, Schneider P, Schneider G. Revealing the Macromolecular Targets of Fragment-Like Natural Products. Angewandte Chemie (International ed in English). 2015 Sep;54(36):10516–20.
• Perna AM, Rodrigues T, Schmidt TP, Böhm M, Stutz K, Reker D, et al. Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor of Helicobacter Pylori HtrA. Angewandte Chemie (International ed in English). 2015 Aug;54(35):10244–8.
• Reker D, Schneider G. Active-learning strategies in computer-assisted drug discovery. Drug discovery today. 2015 Apr;20(4):458–65.
• Miyao T, Reker D, Schneider P, Funatsu K, Schneider G. Chemography of natural product space. Planta medica. 2015 Apr;81(6):429–35.
• Rodrigues T, Hauser N, Reker D, Reutlinger M, Wunderlin T, Hamon J, et al. Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angewandte Chemie (International ed in English). 2015 Jan;54(5):1551–5.
• Reker D, Perna AM, Rodrigues T, Schneider P, Reutlinger M, Mönch B, et al. Revealing the macromolecular targets of complex natural products. Nature chemistry. 2014 Dec;6(12):1072–8.
• Schneider G, Reker D, Rodrigues T, Schneider P. Coping with polypharmacology by computational medicinal chemistry. Chimia. 2014 Sep;68(9):648–53.
• Reker D, Seet M, Pillong M, Koch CP, Schneider P, Witschel MC, et al. Deorphaning pyrrolopyrazines as potent multi-target antimalarial agents. Angewandte Chemie (International ed in English). 2014 Jul;53(27):7079–84.
• Reker D, Rodrigues T, Schneider P, Schneider G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proceedings of the National Academy of Sciences of the United States of America. 2014 Mar;111(11):4067–72.
• Lötsch J, Schneider G, Reker D, Parnham MJ, Schneider P, Geisslinger G, et al. Common non-epigenetic drugs as epigenetic modulators. Trends in molecular medicine. 2013 Dec;19(12):742–53.
• Rodrigues T, Roudnicky F, Koch CP, Kudoh T, Reker D, Detmar M, et al. De novo design and optimization of Aurora A kinase inhibitors. Chemical Science. 2013 Mar 1;4(3):1229–33.
• Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, et al. Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules. Molecular informatics. 2013 Feb;32(2):133–8.
• Reker D, Malmström L. Bioinformatic challenges in targeted proteomics. Journal of proteome research. 2012 Sep;11(9):4393–402.
• Reker D, Katzenbeisser S, Hamacher K. Computation of mutual information from Hidden Markov Models. Computational biology and chemistry. 2010 Dec;34(5–6):328–33.

In The News

• Allowing Machine Learning to Ask Questions Can Make It Smarter (Jul 31, 2023 | Pratt School of Engineering)
• Five Questions for Dan Reker on Bioengineering Better Drug Delivery (Aug 19, 2022 | Duke Government Relations)