The Schürer Systems Drug Discovery Research Group develops and applies data science approaches to chemistry and biology with a focus on developing small molecules with specific biological functions and mechanisms.  Our research programs interface data science, cheminformatics, computational biology, and medicinal chemistry.  To utilize very diverse and large datasets, generate predictive models, and enable others we develop data standards, ontologies, and full stack software applications.

Ontologies and Data Standards

Software Systems

Systems Drug Discovery

Medicinal Chemistry

Our medicinal chemistry team utilizes the tools and workflows designed above to identify targets and hit compounds, and optimize these hits into advanced pre-clinical leads. We are specifically interested in kinase drug discovery, where using machine learning approaches we have identified several first-in-class kinase molecular probes. This work is in collaboration with clinical and biomedical scientists at the Sylvester Comprehensive Cancer Center.

We incorporate computer aided drug design to optimize hit compounds into potent leads. This is done through molecular docking studies, molecular dynamic simulations, Free-energy perturbation studies, and QSAR analysis. Students in medicinal chemistry synthesize small potent libraries of compounds, and also participate in bioassay screening.

Our laboratory is equipped with several chemical fume hoods, a benchtop HPLC-MS, automated purification, benchtop solvent purification, parallel synthesis reactors and common synthetic glassware and equipment.