Publications

Han, Xuexiang, Ying Xu, Adele S. Ricciardi, Junchao Xu, Yan Xiang, Rohan Palanki, Vivek Chowdhary, et al. “Plug-and-play assembly of biodegradable ionizable lipids for potent mRNA delivery and gene editing in vivo.” Proceedings of the National Academy of Sciences of the United States of America 123, no. 22 (June 2026): e2528144123. https://doi.org/10.1073/pnas.2528144123.

Xiang, Yan, Zilu Zhang, Rebeca T. Stiepel, Chinmay S. Potnis, Lauren A. Onweller, Joseph R. Laforet Jr., Hrshita Gowda, and Daniel Reker. “Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning.” American Chemical Society (ACS), April 6, 2026. https://doi.org/10.26434/chemrxiv.15001656/v1.

Markey, Chloe, Zachary Fralish, Hannah Lee, and Daniel Reker. “Ensemble Siamese Neural Networks for Prodrug Activation Prediction.” American Chemical Society (ACS), April 3, 2026. https://doi.org/10.26434/chemrxiv-2025-pn8qg/v2.

Chung, Hong A., Zachary Fralish, Tiffany Tu, and Daniel Reker. “Profiling biological effects of microbiome metabolites via machine learning.” IScience 29, no. 4 (April 2026): 115282. https://doi.org/10.1016/j.isci.2026.115282.

Mestre, Alexander A., Yunju Oh, Jianli Wu, Denise Dunn, Yasaman Setayeshpour, Ssu-Yu Chen, Chao-Chieh Lin, et al. “Identification of 4,5,6,7-Tetrabromo-1H-benzotriazole (TBB) as a Small Molecule MESH1 Inhibitor that Suppresses Ferroptosis.” BioRxiv, February 20, 2026. https://doi.org/10.64898/2026.02.19.706832.

Markey, Chloe, Zachary Fralish, Hannah Lee, and Daniel Reker. “Ensemble Siamese Neural Networks for Prodrug Activation Prediction.” American Chemical Society (ACS), November 27, 2025. https://doi.org/10.26434/chemrxiv-2025-pn8qg.

Kim, Sarah, Taranpreet Kaur, Yulia Shmidov, Matthew Wang, Lixin Fan, Abigail Leo, Yan Xiang, Daniel Reker, and Ashutosh Chilkoti. “Genetically encoded sterol-modification of a synthetic intrinsically disordered protein leads to diverse self-assembly behavior.” American Chemical Society (ACS), October 15, 2025. https://doi.org/10.26434/chemrxiv-2025-5zcxx.

Zhang, Zilu, Yan Xiang, Joe Laforet, Ivan Spasojevic, Ping Fan, Ava Heffernan, Christine E. Eyler, Kris C. Wood, Zachary C. Hartman, and Daniel Reker. “TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery.” ACS Nano 19, no. 37 (September 23, 2025): 33288–96. https://doi.org/10.1021/acsnano.5c09066.

Chung, Hong A., Zachary Fralish, Tiffany Tu, and Daniel Reker. “Profiling Biological Effects of Microbiome Metabolites via Machine Learning.” American Chemical Society (ACS), August 4, 2025. https://doi.org/10.26434/chemrxiv-2025-15mw9.

Gowda, Hrshita, Wenbo Lu, Paul Skaluba, Yan Xiang, Jessica McCann, Laura McCoubrey, John Rawls, Ophelia Venturelli, and Daniel Reker. “Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.” American Chemical Society (ACS), June 11, 2025. https://doi.org/10.26434/chemrxiv-2025-lddpg.

Zhang, Zilu, Yan Xiang, Joe Laforet Jr., Ivan Spasojevic, Ping Fan, Ava Heffernan, Christine E. Eyler, Kris C. Wood, Zachary C. Hartman, and Daniel Reker. “TuNa-AI: a hybrid kernel machine to design tunable nanoparticles for drug delivery.” American Chemical Society (ACS), March 18, 2025. https://doi.org/10.26434/chemrxiv-2025-r8mvz.

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 15, 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.