Computer-Guided Efficient Discovery of Potent Enzyme Inhibitors Drug discovery is being pursued through computer-aided design, synthesis, biological assaying, and crystallography. Lead identification features de novo design with the ligand growing program BOMB or docking of commercial compound libraries. Emphasis is placed on optimization of the resultant leads to yield potent, drug-like inhibitors. Monte Carlo/free-energy perturbation (FEP) simulations are often executed to identify the most promising choices for substituents on rings, heterocycles, and linking groups. Type of Event: Departmental Colloquium Read more about Computer-Guided Efficient Discovery of Potent Enzyme Inhibitors
$2M Grant Awarded to Department for Instrumentation Researchers receive $2M NIH instrumentation grant The National Institutes of Health has awarded researchers in the Department of Chemistry a grant of nearly $2 million for a high-resolution mass spectrometer that will enhance capabilities for scientists in many fields across campus. Read more about $2M Grant Awarded to Department for Instrumentation
Hydrogen and Halogen Bonding: Application, Competition, and Control Hydrogen bonding is one of the most well-characterized non-covalent interactions. Analogous to hydrogen bonding, halogen bonding has become an important focus of study, notably in supramolecular chemistry. The geometric specificity of hydrogen and halogen bonding is often exploited to build crystal frameworks. These two interactions can be used in tandem to created novel organic frameworks. Direct competition occurs between the interactions as the same acceptors can form both interactions. Understanding this Type of Event: Organic Seminar Read more about Hydrogen and Halogen Bonding: Application, Competition, and Control
Covalent Organic Frameworks: Tuning Assembly for Improved Crystallinity and Porosity Type of Event: Organic Seminar Read more about Covalent Organic Frameworks: Tuning Assembly for Improved Crystallinity and Porosity
Mitochondrial Fe-S cluster assembly in Arabidopsis thaliana In plants, three Fe-S cluster assembly pathways exist namely SUF, ISC and CIA, involved in the maturation of iron sulfur proteins in the plastid, mitochondria and cytosol respectively. In this talk, we report on anaerobic purification of three classes of recombinant mitochondrial Fe-S cluster carrier proteins from A. thaliana, namely GRXS15, ISCA1a/2, and NFU4/5, and characterization of their Fe-S cluster content using UV-visible absorption/CD, resonance Raman, EPR, and analytical studies. Type of Event: Inorganic Seminar Read more about Mitochondrial Fe-S cluster assembly in Arabidopsis thaliana
Overcoming Drug Shortages: The Importance of Alternative Synthetic Routes to Anesthetics Type of Event: Organic Seminar Read more about Overcoming Drug Shortages: The Importance of Alternative Synthetic Routes to Anesthetics
Janus: An Extensible Open-Source Software Package for Adaptive QM/MM Methods Type of Event: Physical Seminar Read more about Janus: An Extensible Open-Source Software Package for Adaptive QM/MM Methods
Giant Magnetoresistance Based Spin Valve Sensor for Biomolecule Detection Ever since its discovery in 1988 by two research groups in France and Germany, Giant Magnetoresistance (GMR) has revolutionized the application of magnetic sensors in hard disk drives and magnetic memories.1,2 It also offers an inspiration for their use in magnetic biodetections, a growing field with great promises. Type of Event: Analytical Seminar Read more about Giant Magnetoresistance Based Spin Valve Sensor for Biomolecule Detection
Photoisomerization Dynamics of Stilbenes: Evidence for the Perpendicular Phantom State Type of Event: Physical Seminar Read more about Photoisomerization Dynamics of Stilbenes: Evidence for the Perpendicular Phantom State
PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces Type of Event: Physical Seminar Read more about PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces