Using computational science techniques for exploratory therapies on such areas as neglected diseases and cancer is a very cost-effective approach to finding treatments. Therefore, the continued development of advanced chemotherapeutics is necessary to keep the pipeline full and continue to combat these diseases. As shown in (fig. 3), understanding fundamental relationships ranging from the quantum level can be essential to understanding the overall system. The focus of my research has been using computational chemistry techniques, such as Density Functional Theory (DFT) and Molecular Dynamics (MD), for theoretical design and study of small molecular (organic) compounds, organometallic and inorganic compounds, protein ligand interactions, polymer design and characterization, and drug design. My goal is to employ several computational techniques to understand, as well as predict molecular interactions, e.g. protein ligand-interactions and protein-protein interactions. Some examples of projects I’m working on are, Magnetic Resonance Imaging (MRI) contrast agents, and small molecular inhibitors of Anaplastic Lymphoma Kinase (ALK). The Miller Group intends to expand the capabilities of the Stritch School of Medicine (SSOM) by using High-Performance Computing (HPC) to explore biomedical solutions and process large amounts of scientific data. We intend to apply theoretical work towards open problems, particularly in biomedical research. And, we plan to continue working on collaborations in a wide range of computational applications including neglected disease, medicinal chemistry, computer aided drug design (CADD), bioinformatics, and biophysics. Collaborators and colleagues at other institutions will have the ability to verify our theoretical results via experiment, giving further insight into computational results. The primary research focus is Hit-to-Lead medicinal chemistry, where we will take several approaches, including visualization, transition state modeling, ab initio (Quantum Mechanics), and Molecular Dynamics (MD). The initial step is identification of supplemental lead compounds based on structural similarity to known bioactive compounds available from world-wide databases, e.g. ZINC database2. An underlying principle is to identify chemical entities that have similar chemical or physical behavior with the same receptors. This will be followed up using a technique called molecular docking, a computational procedure that attempts to predict non-covalent binding of small “drug-like” molecules (ligands) to larger macromolecules (receptors) – proteins in this case. This technique can be easily automated as a rapid in-silico screening process to probe libraries of several hundred or thousand compounds. Identified compounds, or “hits”, can then be inspected in further detailed using techniques such as Induced Fit Docking (IFD), where both ligand and identified binding-pocket are energy minimized, generating various binding modes, and identifying crucial amino-acid interactions (fig. 4). Further refinement of protein-ligand interactions can be deduced from Molecular Dynamics (MD) simulations. These resulting lead compounds can again undergo further scrutiny to probe pharmacodynamics and DMPK properties using long-range simulations, e.g. small-molecule membrane transportation interactions. Of course, verification from lab work is always necessary to refine modeling techniques.
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