
About
Raphael Franzini serves as Associate Professor of Medicinal Chemistry at the University of Utah, actively contributing to the Biological Chemistry PhD Program. His research pioneers innovative chemical approaches for therapeutic development, with dual focus on DNA-encoded library technologies and bioorthogonal drug delivery systems.
His educational foundation includes an M.S. from the Swiss Federal Institute of Technology (Lausanne) and a Ph.D. from Stanford University. This training underpins his group's multidisciplinary methodology combining organic synthesis, bioconjugation, computational modeling, and advanced imaging techniques.
Dr. Franzini's research program centers on two transformative areas: First, advancing DNA-encoded library screening through computational integration to identify leads for challenging targets like Tankyrase and Sirtuin 6, with recent work addressing false negatives in machine learning prediction. Second, developing novel bioorthogonal release chemistry using isonitrile-tetrazine reactions for spatiotemporally controlled drug activation, validated in zebrafish models. His group emphasizes both technological innovation and therapeutic translation, with chemistry designed to minimize off-target effects in solid tumors.
Analysis of his 15 most recent publications reveals escalating integration of computational methods with experimental library screening, alongside refinement of bioorthogonal release kinetics. The work spans chemical biology, medicinal chemistry, and pharmaceutical sciences, with growing emphasis on machine learning for library data interpretation and in vivo validation of drug-release systems.
Dr. Franzini maintains an active research laboratory that provides comprehensive training in cutting-edge drug discovery methodologies. His group culture prioritizes both scientific innovation and researcher development, with projects spanning from fundamental reaction kinetics to therapeutic applications. The lab's infrastructure supports organic synthesis, molecular imaging, and computational analysis for advancing precision therapeutics.
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