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Zachary Fralish, Ph.D.

Assistant Professor of Chemistry

Fralish

My goal is to encourage students to gain a deeper appreciation of the science all around us by connecting new concepts to existing mental scaffolding from daily life. With demos, games, and activities sprinkled throughout lectures, students can be introduced to new topics in a relatable way and have their curiosity sparked through learning by doing.

-Zachary Fralish

Polk Science - 214

Biography

Dr. Zachary Fralish started his scientific journey right here at Florida Southern College. He worked on a variety of drug delivery projects, culminating in a U.S. patent for a novel drug-eluting biomaterial designed to reduce complications following surgery, such as infection and inflammation. These experiences inspired him to pursue further research with the goal of designing safer medications. At Duke University, he crafted machine learning algorithms to help guide the design of safer versions of medications. He used these tools to design cancer drugs he validated to have reduced platelet toxicity in vivo and antibiotics that spared commensal (“good”) bacteria but maintained efficacy against pathogenic (“bad”) bacteria.

During his PhD, he found what he enjoyed the most was helping others with their projects and sharing what he had learned. Reflecting on how much he appreciated his well-rounded, liberal arts education, he decided he wanted to teach at small liberal arts college. Now, he has returned to FSC to share his passion for scientific inquiry and lifelong learning with the next generation of scientists.

Education

  • Ph.D., Biomedical Engineering, Duke University
  • B.S., Biochemistry and Molecular Biology, Florida Southern College

Publications

  • Fralish, Z., Reker, D. (2026) “Pairwise Learning for Molecular Property Prediction and Optimization” Front. Drug Discov. 6:1859068.
  • Chung, H., Fralish, Z., Tu, T., Reker, D. (2026) “Profiling Biological Effects of Microbiome Metabolites via Machine Learning” iScience 29(4)
  • Fralish, Z. & Reker, D. (2024). Taking a deep dive with active learning for drug discovery. Nat. Comput. Sci. 4, 727-728.
  • Fralish, Z. & Reker, D. (2024). Finding the most potent compounds using active learning on molecular pairs. Beilstein J. Org. Chem. 20, 2152-2162.
  • Fralish, Z., Skaluba, P., & Reker, D. (2024). Leveraging bounded datapoints to classify molecular potency improvements. RSC Med. Chem. 15, 2474-2482.
  • Khodabukus, A., Prabhu, N., Roberts, T., Buldo, M., DeTwiler, A., Fralish, Z., Kondash, M., Truskey, G., Koves, T., Rufibach, L., Albrecht, E., Williams, B., Bursac, N. (2024). "Bioengineered model of human LGMD2B skeletal muscle reveals roles of intracellular calcium overload in contractile and metabolic dysfunction in dysferlinopathy." Advanced Science 2400188.
  • Fralish, Z., Chen, A., Khan, S. & Reker, D. (2024). The landscape of small-molecule prodrugs. Nat Rev Drug Discov. 23, 365-380.
  • Shi, Y., Reker, D., Byrne, J., Kirtane, A., Hess, K., Wang, Z., Navamajiti, N., Young, C., Fralish, Z., Zhang, Z., Lopes, A., Soares, V., Wainer, J., von Erlach, T., Miao, L., Langer, R., & Traverso, G. (2024). Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning. Nat Biomed Eng. 8, 278-290.
  • Fralish, Z., Chen, A., Skaluba, P., & Reker, D. (2023). DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. J. Cheminform. 15(101).
  • Fralish, Z., Lotz, E. M., Chavez, T., Khodabukus, A., & Bursac, N. (2021). Neuromuscular Development and Disease: Learning From in vitro and in vivo Models. Front. Cell Dev. Biol., 3019.
  • Fralish, Z., Hallmark, N., & Marshall, J. (2021). Using Differential Equations to Model Phoretic Parasitism as Part of SCUDEM Challenge. IEJME, 16(2), em0631.
  • Shelby, S., & Fralish, Z. (2021). Using Edpuzzle to improve student experience and performance in the biochemistry laboratory. Biochem Mol Biol Educ. 49(4), 529-534.
  • Fralish, Z., & Shelby, S. (2020). Peptide examination and study tool: An online learning interface for amino acids and peptides in the introductory biochemistry classroom. Biochem Mol Biol Educ. 48(6), 665-666.
  • Fralish, Z., Tyson III, B., & Stefan, A. (2019). Using Differential Equations to Model Predator-Prey Relations as Part of SCUDEM Modeling Challenge. Rose-Hulman Undergraduate Mathematics Journal, 20(2), 7.