Publications

Fralish, Zachary, and Daniel Reker. “Taking a deep dive with active learning for drug discovery.” Nature Computational Science 4, no. 10 (October 2024): 727–28. https://doi.org/10.1038/s43588-024-00704-6.

Markey, Chloe E., and Daniel Reker. “Machine learning trims the peptide drug design process to a sweet spot.” Nature Chemistry 16, no. 9 (September 2024): 1394–95. https://doi.org/10.1038/s41557-024-01610-0.

Mendes, Bárbara B., Zilu Zhang, João Conniot, Diana P. Sousa, João M. J. M. Ravasco, Lauren A. Onweller, Andżelika Lorenc, Tiago Rodrigues, Daniel Reker, and João Conde. “A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.” Nature Nanotechnology 19, no. 6 (June 2024): 867–78. https://doi.org/10.1038/s41565-024-01673-7.

Li, Z., Y. Xiang, Y. Wen, and D. Reker. “Yoked learning in molecular data science.” Artificial Intelligence in the Life Sciences 5 (June 1, 2024). https://doi.org/10.1016/j.ailsci.2023.100089.

Fralish, Zachary, Ashley Chen, Shaharyar Khan, Pei Zhou, and Daniel Reker. “The landscape of small-molecule prodrugs.” Nat Rev Drug Discov 23, no. 5 (May 2024): 365–80. https://doi.org/10.1038/s41573-024-00914-7.

Shi, Yunhua, Daniel Reker, James D. Byrne, Ameya R. Kirtane, Kaitlyn Hess, Zhuyi Wang, Natsuda Navamajiti, et al. “Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.” Nature Biomedical Engineering 8, no. 3 (March 2024): 278–90. https://doi.org/10.1038/s41551-023-01128-9.

Navamajiti, Natsuda, Apolonia Gardner, Ruonan Cao, Yutaro Sugimoto, Jee Won Yang, Aaron Lopes, Nhi V. Phan, et al. “Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery.” Journal of Pharmaceutical Sciences 113, no. 3 (March 2024): 718–24. https://doi.org/10.1016/j.xphs.2023.09.001.

Markey, Chloe, Samuel Croset, Olivia Ruth Woolley, Can Martin Buldun, Christian Koch, Daniel Koller, and Daniel Reker. “Characterizing emerging companies in computational drug development.” Nature Computational Science 4, no. 2 (February 2024): 96–103. https://doi.org/10.1038/s43588-024-00594-8.

Fralish, Zachary, and Daniel Reker. “Finding the most potent compounds using active learning on molecular pairs.” Beilstein Journal of Organic Chemistry 20 (January 2024): 2152–62. https://doi.org/10.3762/bjoc.20.185.

Mullowney, Michael W., Katherine R. Duncan, Somayah S. Elsayed, Neha Garg, Justin J. J. van der Hooft, Nathaniel I. Martin, David Meijer, et al. “Artificial intelligence for natural product drug discovery.” Nature Reviews. Drug Discovery 22, no. 11 (November 2023): 895–916. https://doi.org/10.1038/s41573-023-00774-7.

Fralish, Zachary, Ashley Chen, Paul Skaluba, and Daniel Reker. “DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.” Journal of Cheminformatics 15, no. 1 (October 2023): 101. https://doi.org/10.1186/s13321-023-00769-x.

Li, Zhixiong, Yan Xiang, Yujing Wen, and Daniel Reker. “Yoked Learning in Molecular Data Science.” American Chemical Society (ACS), August 16, 2023. https://doi.org/10.26434/chemrxiv-2023-80fd7.

Wen, Y., Z. Li, Y. Xiang, and D. Reker. “Improving molecular machine learning through adaptive subsampling with active learning.” Digital Discovery 2, no. 4 (August 1, 2023): 1134–42. https://doi.org/10.1039/d3dd00037k.

Xiang, Yan, Yu-Hang Tang, Guang Lin, and Daniel Reker. “Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.” Journal of Chemical Information and Modeling 63, no. 15 (August 2023): 4633–40. https://doi.org/10.1021/acs.jcim.3c00396.

Fralish, Zachary, Ashley Chen, Paul Skaluba, and Daniel Reker. “DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning.” American Chemical Society (ACS), April 11, 2023. https://doi.org/10.26434/chemrxiv-2023-gbchq.

Xiang, Yan, Yu-Hang Tang, Guang Lin, and Daniel Reker. “Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.” American Chemical Society (ACS), February 20, 2023. https://doi.org/10.26434/chemrxiv-2023-gd1gl.

Wen, Yujing, Zhixiong Li, Yan Xiang, and Daniel Reker. “Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning.” American Chemical Society (ACS), February 13, 2023. https://doi.org/10.26434/chemrxiv-2023-h8905.

Abramson, A., A. R. Kirtane, Y. Shi, G. Zhong, J. E. Collins, S. Tamang, K. Ishida, et al. “Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections.” Matter 5, no. 3 (March 2, 2022): 975–87. https://doi.org/10.1016/j.matt.2021.12.022.

Shi, Yunhua, Chih-Hsin Lin, Daniel Reker, Christoph Steiger, Kaitlyn Hess, Joy Collins, Siddartha Tamang, et al. “A machine learning liver-on-a-chip system for safer drug formulation.” BioRxiv, 2022. https://doi.org/10.1101/2022.09.05.506668.

Wollborn, Jakob, Lars O. Hassenzahl, Daniel Reker, Hans Felix Staehle, Anne Marie Omlor, Wolfgang Baar, Kai B. Kaufmann, et al. “Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection.” Annals of Intensive Care 11, no. 1 (December 2021): 175. https://doi.org/10.1186/s13613-021-00965-8.

Steiger, Christoph, Nhi V. Phan, Hen-Wei Huang, Haoying Sun, Jacqueline N. Chu, Daniel Reker, Declan Gwynne, et al. “Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors.” Advanced Science (Weinheim, Baden-Wurttemberg, Germany) 8, no. 24 (December 2021): e2102861. https://doi.org/10.1002/advs.202102861.

Lee, K., A. Yang, Y. C. Lin, D. Reker, G. J. L. Bernardes, and T. Rodrigues. “Combating small-molecule aggregation with machine learning.” Cell Reports Physical Science 2, no. 9 (September 22, 2021). https://doi.org/10.1016/j.xcrp.2021.100573.

Reker, Daniel, Yulia Rybakova, Ameya R. Kirtane, Ruonan Cao, Jee Won Yang, Natsuda Navamajiti, Apolonia Gardner, et al. “Computationally guided high-throughput design of self-assembling drug nanoparticles.” Nature Nanotechnology 16, no. 6 (June 2021): 725–33. https://doi.org/10.1038/s41565-021-00870-y.

Reker, D. “Chapter 14: Active Learning for Drug Discovery and Automated Data Curation.” In RSC Drug Discovery Series, 2021-January:301–26, 2021. https://doi.org/10.1039/9781788016841-00301.

Reker, D., E. A. Hoyt, G. J. L. Bernardes, and T. Rodrigues. “Adaptive Optimization of Chemical Reactions with Minimal Experimental Information.” Cell Reports Physical Science 1, no. 11 (November 18, 2020). https://doi.org/10.1016/j.xcrp.2020.100247.