Roughly half of the drugs in clinical use today started as natural products — molecules that evolved inside microorganisms and plants that form the backbone of antibiotics, anti-cancer agents and other medicines.
Over the past decade, the University of Michigan has become a leader in natural product sciences.
The LSI's Natural Products Discovery Core has developed a 45,000-sample (and growing) library of natural product extracts derived from a unique collection of diverse marine and terrestrial actinomycetes, fungi and cyanobacteria. The core provides researchers at U-M and external partners with the technology and expertise to develop candidates identified through high-throughput screening into unique, bioactive, patentable, small molecules.
Rapid genomic and metabolomic profiling allows users to identify high value molecules as probes and drug leads.
Recent investments by the U-M Biosciences Initiative will add state-of-the art mass spectrometry and NMR resources for structure elucidation, as well as the recruitment of new faculty and specialists.
Congratulations to Ralph "Jake" Harte for being selected to the 2021 Rosen Fellows program. Jake will be continuing with the NPDC this summer working on a variety of research projects.
Welcome to Fei Yang, our new Postdoctoral Research Fellow. Fei joins us from Wayne State University where he earned his Ph.D. and continued his research until we were able to welcome him to the NPDC. Please join us in welcoming Fei to the team!
More than 45,000 natural product extracts collected around the globe. Available for high-throughput screening in the U-M Center for Chemical Genomics.
Bioactive molecule identification using traditional bio-assay guided fractionation, as well as new data-guided discovery tools. Small-molecule structural characterization. Optimization for creating intellectual property.
Ability to do high-throughput molecular characterization of enzymatic products, and analysis using rapid separation technologies.
Biosynthetic cluster mining of microbial genomic DNA. Artificial-intelligence & machine learning-based genome-to-natural-product technologies.