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.
Do you have a natural product project you would like to receive funding for? Great news! The Michigan Drug Discovery (MDD) is accepting applications for its Project Grants ($100,000 per year for up to three years) and Screening Grants ($75,000 for one year or $100,000 for two years). A pre-submission meeting with the MDD director is encouraged prior to applying; all submissions are due by May 17, 2022.
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.